diff --git a/chemfusekit/__base.py b/chemfusekit/__base.py
index e024422..6d3fc8a 100644
--- a/chemfusekit/__base.py
+++ b/chemfusekit/__base.py
@@ -92,8 +92,15 @@ def __getitem__(self, index):
 class BaseSettings:
     """Holds the settings for all objects with settings."""
 
-    def __init__(self, output: GraphMode = GraphMode.NONE):
-        self.output = output
+    def __init__(self, output: str = 'none'):
+        if output == 'none':
+            self.output = GraphMode.NONE
+        elif output == 'graphical':
+            self.output = GraphMode.GRAPHIC
+        elif output == 'text':
+            self.output = GraphMode.TEXT
+        else:
+            raise ValueError("The output mode should be 'none', 'graphical' or 'text'.")
 
 
 class BaseActionClass(ABC):
@@ -145,9 +152,9 @@ def export_model(self, export_path: str):
 class BaseClassifierSettings(BaseSettings):
     """Holds the settings for the BaseClassifier object."""
 
-    def __init__(self, output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, output: str = 'none', test_split: bool = False):
         super().__init__(output)
-        if test_split is True and output is GraphMode.NONE:
+        if test_split is True and self.output is GraphMode.NONE:
             raise Warning(
                 "You selected test_split but it won't run because you disabled the output."
             )
diff --git a/chemfusekit/df.py b/chemfusekit/df.py
index fb1ae04..89d58bc 100644
--- a/chemfusekit/df.py
+++ b/chemfusekit/df.py
@@ -7,7 +7,8 @@
 
 import matplotlib.pyplot as plt
 
-from .__base import GraphMode, BaseDataModel, BaseSettings
+from .__base import BaseDataModel, BaseSettings
+from .__utils import GraphMode
 from .pca import PCASettings, PCA
 from .plsda import PLSDASettings, PLSDA
 
@@ -37,7 +38,7 @@ def __init__(self, x_data: pd.DataFrame, x_train: pd.DataFrame, y: np.ndarray,
 class DFSettings(BaseSettings):
     """Holds the settings for the DF object."""
 
-    def __init__(self, output: GraphMode = GraphMode.NONE, method: str = 'concat'):
+    def __init__(self, output: str = 'none', method: str = 'concat'):
         super().__init__(output)
         if method not in ['concat', 'outer']:
             raise ValueError("DF: invalid method: must be 'concat' or 'outer'")
diff --git a/chemfusekit/knn.py b/chemfusekit/knn.py
index b74deaa..3a7cdf7 100644
--- a/chemfusekit/knn.py
+++ b/chemfusekit/knn.py
@@ -11,7 +11,7 @@
 class KNNSettings(BaseClassifierSettings):
     """Holds the settings for the kNN object."""
     def __init__(self, n_neighbors: int = 15, metric: str | Callable = 'euclidean', weights: str | Callable = 'uniform',
-                 algorithm: str = 'auto', output: GraphMode = GraphMode.NONE, test_split: bool = False):
+                 algorithm: str = 'auto', output: str = 'none', test_split: bool = False):
 
         super().__init__(output, test_split)
 
@@ -68,7 +68,7 @@ def train(self):
             self.settings.output
         )
 
-        if self.settings.test_split and self.settings.output:
+        if self.settings.test_split and self.settings.output is not GraphMode.NONE:
             knn_split = KNeighborsClassifier(
                 n_neighbors=self.settings.n_neighbors,
                 metric=self.settings.metric,
diff --git a/chemfusekit/lda.py b/chemfusekit/lda.py
index 87f8a73..ff7f112 100644
--- a/chemfusekit/lda.py
+++ b/chemfusekit/lda.py
@@ -9,8 +9,8 @@
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LD
 from sklearn.model_selection import cross_val_score
 
-from chemfusekit.__utils import graph_output, run_split_test
-from chemfusekit.__utils import print_confusion_matrix, print_table, GraphMode
+from chemfusekit.__utils import graph_output, run_split_test, GraphMode
+from chemfusekit.__utils import print_confusion_matrix, print_table
 from .__base import BaseDataModel, BaseClassifier, BaseClassifierSettings, BaseReducer, ReducerDataModel
 
 
@@ -25,7 +25,7 @@ def __init__(self, x_data: pd.DataFrame, x_train: pd.DataFrame, y: np.ndarray, c
 class LDASettings(BaseClassifierSettings):
     """Holds the settings for the LDA object."""
 
-    def __init__(self, components: int | None = None, output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, components: int | None = None, output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if components is not None and components <= 2:
             raise ValueError("Invalid component number: must be a > 1 integer.")
diff --git a/chemfusekit/lr.py b/chemfusekit/lr.py
index c28b345..d11dc30 100644
--- a/chemfusekit/lr.py
+++ b/chemfusekit/lr.py
@@ -5,13 +5,13 @@
 import pandas as pd
 from sklearn.linear_model import LogisticRegression
 
-from chemfusekit.__utils import run_split_test, print_confusion_matrix, print_table, GraphMode
+from chemfusekit.__utils import run_split_test, print_confusion_matrix, print_table
 from .__base import BaseClassifierSettings, BaseDataModel, BaseClassifier, ReducerDataModel
 
 
 class LRSettings(BaseClassifierSettings):
     """Holds the settings for the LR object."""
-    def __init__(self, algorithm: str = 'liblinear', output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, algorithm: str = 'liblinear', output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if algorithm not in [
             'lbfgs',
diff --git a/chemfusekit/pca.py b/chemfusekit/pca.py
index ea0aefb..6f563f0 100644
--- a/chemfusekit/pca.py
+++ b/chemfusekit/pca.py
@@ -18,9 +18,8 @@
 class PCASettings(BaseSettings):
     """Holds the settings for the PCA object."""
 
-    def __init__(self, target_variance: float = 0.95,
-                 confidence_level: float = 0.05,
-                 initial_components: int = 10, output: GraphMode = GraphMode.NONE):
+    def __init__(self, target_variance: float = 0.95, confidence_level: float = 0.05, initial_components: int = 10,
+                 output: str = 'none'):
         super().__init__(output)
         if target_variance < 0:
             raise ValueError("Target variance should be positive or null.")
diff --git a/chemfusekit/plsda.py b/chemfusekit/plsda.py
index 2eb05a8..fe92676 100644
--- a/chemfusekit/plsda.py
+++ b/chemfusekit/plsda.py
@@ -8,14 +8,14 @@
 from sklearn.cross_decomposition import PLSRegression as PLSR
 from sklearn.model_selection import cross_val_score
 
-from chemfusekit.__utils import GraphMode, print_table, print_confusion_matrix, run_split_test
+from chemfusekit.__utils import print_table, print_confusion_matrix, run_split_test, GraphMode
 from .__base import BaseClassifierSettings, BaseDataModel, BaseClassifier, BaseReducer, ReducerDataModel
 
 
 class PLSDASettings(BaseClassifierSettings):
     """Holds the settings for the PLSDA object."""
 
-    def __init__(self, components: int | None = None, output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, components: int | None = None, output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if components is not None and components < 1:
             raise ValueError("Invalid n_components number: should be a positive integer.")
@@ -136,7 +136,7 @@ def train(self):
         if self.settings.output and self.settings.test_split:
             x = self.data.x_data
             y = self.data.x_train.Substance.astype('category').cat.codes
-            run_split_test(x, y, PLSR(self.settings.components), mode=self.settings.output)
+            run_split_test(x, y, PLSR(self.components), mode=self.settings.output)
 
     def _select_feature_number(self, x, y):
         # Auto-select the number of components
diff --git a/chemfusekit/svm.py b/chemfusekit/svm.py
index 6114eec..aaf4836 100644
--- a/chemfusekit/svm.py
+++ b/chemfusekit/svm.py
@@ -3,13 +3,13 @@
 
 from sklearn.svm import SVC
 
-from chemfusekit.__utils import GraphMode, run_split_test, print_confusion_matrix
+from chemfusekit.__utils import run_split_test, print_confusion_matrix
 from .__base import BaseClassifierSettings, BaseClassifier, BaseDataModel
 
 
 class SVMSettings(BaseClassifierSettings):
     """Holds the settings for the SVM object."""
-    def __init__(self, kernel: str = 'linear', output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, kernel: str = 'linear', output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if kernel not in ['linear', 'poly', 'gaussian', 'sigmoid']:
             raise ValueError("Invalid type: must be linear, poly, gaussian or sigmoid")
diff --git a/docs/docs/base/basesettings.md b/docs/docs/base/basesettings.md
index 76b28b0..aea815f 100644
--- a/docs/docs/base/basesettings.md
+++ b/docs/docs/base/basesettings.md
@@ -10,11 +10,11 @@ Holds the settings for all classifier object. It's not meant for direct usage, o
 ## Syntax
 
 ```python
-BaseSettings(output: GraphMode, test_split: false)
+BaseSettings(output: str, test_split: false)
 ```
 
 ## Fields and constructor parameters
-- `output`: toggles graph output mode. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to be set to `True` to work.
 
 The constructor raises:
diff --git a/docs/docs/base/reducerdatamodel.md b/docs/docs/base/reducerdatamodel.md
index 09df8c5..2fe49b9 100644
--- a/docs/docs/base/reducerdatamodel.md
+++ b/docs/docs/base/reducerdatamodel.md
@@ -5,9 +5,9 @@ sidebar-position: 6
 
 # ReducerDataModel class
 
-This class models the output data for all dimensionality reduction operations (currently, the [`LDA`](../lda/lda), the [`PLSDA`](../plsda/plsda.md) and the [`PCA`](../pca/pca.md) operations).
+This class models the output data for all dimensionality reduction operations (currently, the [`LDA`](../lda/lda.md), the [`PLSDA`](../plsda/plsda.md) and the [`PCA`](../pca/pca.md) operations).
 
-It inherits from [`BaseDataModel`](../base/basedatamodel.md).
+It inherits from [`BaseDataModel`](./basedatamodel.md).
 
 ## Syntax
 
diff --git a/docs/docs/complete-workflow.md b/docs/docs/complete-workflow.md
index f607520..d8a9f38 100644
--- a/docs/docs/complete-workflow.md
+++ b/docs/docs/complete-workflow.md
@@ -5,25 +5,25 @@ sidebar-position: 7
 # Complete workflow
 
 Here's a sequence diagram to represent an example workflow, from the raw data
-tables to classification, including data fusion, PCA and training.
+tables to classification, including data fusion, dimensionality reduction and training.
 
 ```plantuml
 actor User
-participant LLDF
-participant PCA
+participant DF
+participant Reducer
 participant Classifier
 
-User -> LLDF : Upload training tables
-User -> LLDF : Set parameters
+User -> DF : Upload training tables
+User -> DF : Set parameters
 User -> Classifier : (optional) Upload model
 
-LLDF -> PCA  : Pass preprocessed / fused tables
-LLDF --> User  : Download fused tables
-LLDF -> Classifier  : Pass preprocessed / fused tables \nRun classification
-PCA -> Classifier : (optional) Set number of components
+DF -> Reducer  : Pass preprocessed / fused tables
+DF --> User  : Download fused tables
+DF -> Classifier  : Pass preprocessed / fused tables \nRun classification
+Reducer -> Classifier : (optional) Set number of components
 
 Classifier --> User : classification results, graphs
-PCA --> User : classification results, graphs
+Reducer --> User : classification results, graphs
 Classifier --> User : (optional) download trained model
 
 User -> Classifier : pass data to classify
diff --git a/docs/docs/df/dfsettings.md b/docs/docs/df/dfsettings.md
index a82f1f2..e6c20aa 100644
--- a/docs/docs/df/dfsettings.md
+++ b/docs/docs/df/dfsettings.md
@@ -2,18 +2,18 @@
 sidebar_position: 2
 ---
 
-# LLDFSettings class
+# DFSettings class
 
 Holds the settings for the [`DF`](./df-class.md) object.
 
 ## Syntax
 
 ```python
-DFSettings(output: GraphMode, method: str)
+DFSettings(output: str, method: str)
 ```
 
 ## Fields and constructor parameters
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `method`: a choice between `concat` (concatenation-based data fusion) and `outer` (outer matrix multiplication-based data fusion)
 
 The constructor raises a `ValueError("DF: invalid method: must be 'concat' or 'outer'")` if an invalid parameter is provided to the `method` field.
@@ -24,5 +24,5 @@ The constructor raises a `ValueError("DF: invalid method: must be 'concat' or 'o
 from chemfusekit.df import DFSettings
 
 # Initialize the settings for low-level data fusion
-df_settings = DFSettings(output=GraphMode.TEXT)
+df_settings = DFSettings(output='text')
 ```
\ No newline at end of file
diff --git a/docs/docs/knn/knn.md b/docs/docs/knn/knn.md
index 01dafcf..556c992 100644
--- a/docs/docs/knn/knn.md
+++ b/docs/docs/knn/knn.md
@@ -20,9 +20,9 @@ KNN(settings: KNNSettings, data: LLDFModel)
 
 ## Fields
 
-- `settings`: object of type [`KNNSettings`](/tesi/docs/knn/knnsettings). Contains the settings for
+- `settings`: object of type [`KNNSettings`](./knnsettings.md). Contains the settings for
   the `KNN` object. 
-- `data`: onject of type ['DFModel`](/tesi/docs/df/dfmodel). Contains the
+- `data`: onject of type ['DFModel`](../df/dfmodel.md). Contains the
   artifacts from the data fusion process.
 - `model`: a `KNeighborsClassifier` model from `scikit-learn`. Defaults to `None`.
 
diff --git a/docs/docs/knn/knnsettings.md b/docs/docs/knn/knnsettings.md
index 81f9486..806d216 100644
--- a/docs/docs/knn/knnsettings.md
+++ b/docs/docs/knn/knnsettings.md
@@ -16,7 +16,7 @@ KNNSettings(
   metric: str | Callable,
   weights: str | Callable,
   algorithm: str,
-  output: GraphMode,
+  output: str,
   test_split: false
 )
 ```
@@ -38,7 +38,7 @@ KNNSettings(
     - `kd_tree`
     - `brute`
   or be a callable object.
-- `output`: toggles graph output mode. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to be set to `True` to work.
 
 The constructor raises:
@@ -51,14 +51,14 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.knn import KNNSettings, GraphMode
+from chemfusekit.knn import KNNSettings
 
 settings = KNNSettings(
     n_neighbors=20,     # pick 20 neighbors
-    metric='minkowski', # choose the metric
-    weights='distance', # choose the weight metric
+    metric='minkowski',   # choose the metric
+    weights='distance',   # choose the weight metric
     algorithm='auto',   # the best algorithm gets chosen automatically
-    output=GraphMode.GRAPHIC, # graph output is enabled
-    test_split=True # the model will be split-tested at the end of the training
+    output='graphical',   # graph output is enabled
+    test_split=True   # the model will be split-tested at the end of the training
 )
 ```
\ No newline at end of file
diff --git a/docs/docs/lda/ldasettings.md b/docs/docs/lda/ldasettings.md
index c9c850e..db7d26b 100644
--- a/docs/docs/lda/ldasettings.md
+++ b/docs/docs/lda/ldasettings.md
@@ -11,13 +11,13 @@ Inherits from [`BaseSettings`](../base/basesettings.md).
 ## Syntax
 
 ```python
-LDASettings(components: int, output: GraphMode, split_test: bool)
+LDASettings(components: int, output: str, split_test: bool)
 ```
 
 ## Fields and constructor parameters
 
 - `components`: the amount of components to be used in the LDA model. Defaults to 3.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles split testing. Defaults to `False`.
 
 
@@ -29,11 +29,11 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.lda import LDASettings, GraphMode
+from chemfusekit.lda import LDASettings
 
 settings = LDASettings(
     components=(pca.components - 1),    # one less component than the number determined by PCA
-    output=GraphMode.GRAPHIC, # graphs will be printed
-    test_split=True # split testing is enabled
+    output='graphical',   # graphs will be printed
+    test_split=True   # split testing is enabled
 )
 ```
\ No newline at end of file
diff --git a/docs/docs/lr/lrsettings.md b/docs/docs/lr/lrsettings.md
index 847c002..7508a26 100644
--- a/docs/docs/lr/lrsettings.md
+++ b/docs/docs/lr/lrsettings.md
@@ -11,7 +11,7 @@ Inherits from [`BaseSettings`](../base/basesettings.md).
 ## Syntax
 
 ```python
-LRSettings(algorithm: str, output: GraphMode, test_split: bool)
+LRSettings(algorithm: str, output: str, test_split: bool)
 ```
 
 ## Fields and constructor parameters
@@ -23,7 +23,7 @@ LRSettings(algorithm: str, output: GraphMode, test_split: bool)
     - `newton-cholesky`
     - `sag`
     - `saga`
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles split testing. Defaults to `False`.
 
 The constructor raises:
@@ -34,11 +34,11 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.lr import LRSettings, GraphMode
+from chemfusekit.lr import LRSettings
 
 settings = LRSettings(
     algorithm='newton-cg',
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=True # split testing is enabled
+    output='graphical',   # graphs will be printed
+    test_split=True   # split testing is enabled
 )
 ```
\ No newline at end of file
diff --git a/docs/docs/pca/pca.md b/docs/docs/pca/pca.md
index b3d427e..4618fd1 100644
--- a/docs/docs/pca/pca.md
+++ b/docs/docs/pca/pca.md
@@ -21,7 +21,7 @@ PCA(settings: PCASettings, data: BaseDataModel)
 
 ## Fields
 
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md.md). Contains the data to be analyzed.
+- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
 - `components`: Number of components for the PCA analysis. Defaults to 0.
 - `model`: A `PCA` model from `scikit-learn`. Defaults to `None`.
 - `settings`: object of type [`PCASettings`](./pcasettings.md). Contains the settings for
diff --git a/docs/docs/pca/pcasettings.md b/docs/docs/pca/pcasettings.md
index ca533ea..72d8a38 100644
--- a/docs/docs/pca/pcasettings.md
+++ b/docs/docs/pca/pcasettings.md
@@ -13,7 +13,7 @@ PCASettings(
   target_variance: float,
   confidence_level: float,
   initial_components: int,
-  output: GraphMode
+  output: str 
 )
 ```
 
@@ -24,18 +24,18 @@ PCASettings(
 - `confidence_level`: the confidence level for statistical tests. Defaults to 0.05.
 - `initial_components`: the minimum amount of components to be used in the PCA model.
   Defaults to 10.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 
 ## Example
 
 ```python
-from chemfusekit.pca import PCASettings, GraphMode
+from chemfusekit.pca import PCASettings
 
 # Initialize the settings for Principal Component Analysis
 pca_settings = PCASettings(
     target_variance=0.99,
     confidence_level=0.05,
     initial_components=10,
-    output=GraphMode.GRAPHIC  # graphs will be printed
+    output='graphical'  # graphs will be printed
 )
 ```
\ No newline at end of file
diff --git a/docs/docs/plsda/plsda.md b/docs/docs/plsda/plsda.md
index ca2655d..376317d 100644
--- a/docs/docs/plsda/plsda.md
+++ b/docs/docs/plsda/plsda.md
@@ -24,7 +24,7 @@ PLSDA(settings: PLSDASettings, data: BaseDataModel)
 
 - `settings`: object of type [`PLSDASettings`](./plsdasettings.md). Contains the settings for
   the `PLSDA` object. 
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md.md). Contains the data to be analyzed.
+- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
 - `model`: a `PLSRegression` model from `scikit-learn`. Defaults to `None`.
 
 ## Methods
diff --git a/docs/docs/plsda/plsdasettings.md b/docs/docs/plsda/plsdasettings.md
index 7b547a7..5120699 100644
--- a/docs/docs/plsda/plsdasettings.md
+++ b/docs/docs/plsda/plsdasettings.md
@@ -11,13 +11,13 @@ Inherits from [`BaseSettings`](../base/basesettings.md).
 ## Syntax
 
 ```python
-PLSDASettings(n_components: int, output: GraphMode, test_split: bool)
+PLSDASettings(n_components: int, output: str, test_split: bool)
 ```
 
 ## Fields and constructor parameters
 
 - `n_components`: number of components for the PLSDA analysis. Defaults to 3.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to not be set to `GraphMode.NONE` to work.
 
 The constructor raises:
@@ -27,12 +27,12 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.plsda import PLSDASettings, GraphMode
+from chemfusekit.plsda import PLSDASettings
 
 # Initialize the settings for Partial Least Squares Discriminant Analysis
 plsda_settings = PLSDASettings(
     n_components=5,
-    output=GraphMode.GRAPHIC,   # graphs will be printed
+    output='graphical',     # graphs will be printed
     test_split=False    # no split testing
 )
 ```
\ No newline at end of file
diff --git a/docs/docs/svm/svmsettings.md b/docs/docs/svm/svmsettings.md
index 7ebf852..1c72b57 100644
--- a/docs/docs/svm/svmsettings.md
+++ b/docs/docs/svm/svmsettings.md
@@ -11,7 +11,7 @@ Inherits from [`BaseSettings`](../base/basesettings.md).
 ## Syntax
 
 ```python
-SVMSettings(kernel: str, output: GraphMode, test_split: bool)
+SVMSettings(kernel: str, output: str, test_split: bool)
 ```
 
 ## Fields and constructor parameters
@@ -22,7 +22,7 @@ SVMSettings(kernel: str, output: GraphMode, test_split: bool)
   - `gaussian`
   - `sigmoid`
   Defaults to `linear`.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles split testing. Defaults to `False`.
 
 The constructor raises:
@@ -32,12 +32,12 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.svm import SVMSettings, GraphMode
+from chemfusekit.svm import SVMSettings
 
 # Initialize the settings for Support Vector Machine
 svm_settings = SVMSettings(
     type='linear',
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=True # split testing is enabled
+    output='graphical',   # graphs will be printed
+    test_split=True  # split testing is enabled
 )
 ```
\ No newline at end of file
diff --git a/docs/docs/tutorial.md b/docs/docs/tutorial.md
index 163fa6a..3fa0407 100644
--- a/docs/docs/tutorial.md
+++ b/docs/docs/tutorial.md
@@ -15,19 +15,19 @@ pip install chemfusekit
 
 ## First step: data fusion
 
-We will load the `LLDFSettings` with the paths to some Excel datasheets, containing
+We will load the `DFSettings` with the paths to some Excel datasheets, containing
 respectively the data from a QEPAS spectrometer and a GC chromatographer.
 
 We will pick normalization as the preprocessing technique for the data.
 
-The `LLDF` class will take these settings and perform low-level data fusion on the
+The `DF` class will take these settings and perform low-level data fusion on the
 two Excel tables we picked.
 
 ```python
 from chemfusekit.df import DFSettings, DF
 
 # Initialize the settings for low-level data fusion
-lldf_settings = DFSettings(
+df_settings = DFSettings(
     qepas_path='tests/qepas.xlsx',
     qepas_sheet='Sheet1',
     rt_path='tests/rt.xlsx',
@@ -36,15 +36,15 @@ lldf_settings = DFSettings(
 )
 
 # Initialize and run low-level data fusion
-lldf = DF(lldf_settings)
-lldf.lldf()
+df = DF(df_settings)
+df.train()
 ```
 
 Optionally, we can export the fused data into a new, single Excel datasheet:
 
 ```python
 # (optional) export the LLDF data to an Excel file
-lldf.export_data('output_file.xlsx')
+df.export_data('output_file.xlsx')
 ```
 
 ## Second step: PCA
@@ -62,11 +62,11 @@ pca_settings = PCASettings(
     target_variance=0.99,  # the minimum acceptable level of cumulative explained covariance
     confidence_level=0.05,  # the desired level of confidence
     initial_components=10,  # the initial amount of components for the iterative analysis
-    output=GraphMode.GRAPHIC  # graphs will be printed
+    output='graphical'  # graphs will be printed
 )
 
 # Initialize and run the PCA class
-pca = PCA(lldf.fused_data, pca_settings)
+pca = PCA(df.fused_data, pca_settings)
 pca.train()
 
 # Print the number of components and the statistics
@@ -84,12 +84,12 @@ from chemfusekit.lda import LDASettings, LDA
 
 settings = LDASettings(
     components=(pca.components - 1),  # one less component than the number determined by PCA
-    output=GraphMode.GRAPHIC,  # graphs will be printed
+    output='graphical',     # graphs will be printed
     test_split=True  # Split testing is enabled
 )
 
 # Initialize and run the LDA class
-lda = LDA(lldf.fused_data, settings)
+lda = LDA(df.fused_data, settings)
 lda.train()
 ```
 
@@ -100,8 +100,7 @@ can identify it correctly.
 
 ```python
 # Let's pick a random sample from the dataset and see if it gets recognized correctly:
-x_data_sample = lldf.fused_data.x_train.iloc[119] # should be DMMP
-x_data_sample = x_data_sample.iloc[1:].to_frame().transpose()
+x_data_sample = lldf.fused_data[119] #   should be DMMP
 
 # Let's run the prediction:
 predictions = lda.predict(x_data_sample)
diff --git a/examples/knn_notebook.ipynb b/examples/knn_notebook.ipynb
index 57a54ce..971e381 100644
--- a/examples/knn_notebook.ipynb
+++ b/examples/knn_notebook.ipynb
@@ -74,9 +74,9 @@
    "metadata": {},
    "source": [
     "## First step: Low-Level Data Fusion\n",
-    "- the `LLDF` class is used for data fusion\n",
-    "- the `LLDF_Settings` class is a helper class for setting up `LLDF`\n",
-    "- `LLDF` data can then be exported, or used for further processing"
+    "- the `DF` class is used for data fusion\n",
+    "- the `DFSettings` class is a helper class for setting up `DF`\n",
+    "- `DF` data can then be exported, or used for further processing"
    ]
   },
   {
@@ -106,11 +106,11 @@
     }
    ],
    "source": [
-    "from chemfusekit.lldf import LLDFSettings, LLDF, Table, GraphMode\n",
+    "from chemfusekit.df import DFSettings, DF, Table\n",
     "\n",
     "# Initialize the settings for low-level data fusion\n",
     "# Perform preliminary data fusion\n",
-    "lldf_settings = LLDFSettings(output=GraphMode.GRAPHIC)\n",
+    "df_settings = DFSettings(output='graphical')\n",
     "qepas_table = Table(\n",
     "    file_path=\"qepas.xlsx\",\n",
     "    sheet_name=\"Sheet1\",\n",
@@ -125,8 +125,8 @@
     "tables = [qepas_table, rt_table]\n",
     "\n",
     "# Initialize and run low-level data fusion\n",
-    "lldf = LLDF(lldf_settings, tables)\n",
-    "lldf.lldf()"
+    "df = DF(df_settings, tables)\n",
+    "df.fuse()"
    ]
   },
   {
@@ -136,7 +136,7 @@
    "outputs": [],
    "source": [
     "# (optional) export the LLDF data to an Excel file\n",
-    "lldf.export_data('output_file.xlsx')"
+    "df.export_data('output_file.xlsx')"
    ]
   },
   {
@@ -3415,7 +3415,7 @@
     }
    ],
    "source": [
-    "from chemfusekit.knn import KNNSettings, KNN, GraphMode\n",
+    "from chemfusekit.knn import KNNSettings, KNN\n",
     "\n",
     "# Initialize the settings for Principal Component Analysis\n",
     "knn_settings = KNNSettings(\n",
@@ -3423,13 +3423,13 @@
     "    metric='euclidean',\n",
     "    weights='uniform',\n",
     "    algorithm='auto',\n",
-    "    output=GraphMode.GRAPHIC,   # graphs will be printed as pictures\n",
+    "    output='graphical',   # graphs will be printed as pictures\n",
     "    test_split=True # Split testing will be carried out\n",
     ")\n",
     "\n",
     "# Initialize and run the PCA class\n",
-    "knn = KNN(settings=knn_settings, data=lldf.fused_data)\n",
-    "knn.knn()"
+    "knn = KNN(settings=knn_settings, data=df.fused_data)\n",
+    "knn.train()"
    ]
   },
   {
@@ -3456,7 +3456,7 @@
    ],
    "source": [
     "# Let's pick a random sample and see if it gets recognized correctly:\n",
-    "x_data_sample = lldf.fused_data[119] # should be DMMP\n",
+    "x_data_sample = df.fused_data[119] # should be DMMP\n",
     "\n",
     "# Let's run the prediction:\n",
     "predictions = knn.predict(x_data_sample)\n",
diff --git a/examples/pca_lda_notebook.ipynb b/examples/pca_lda_notebook.ipynb
index 4016dc2..7d96276 100644
--- a/examples/pca_lda_notebook.ipynb
+++ b/examples/pca_lda_notebook.ipynb
@@ -74,9 +74,9 @@
    "metadata": {},
    "source": [
     "## First step: Low-Level Data Fusion\n",
-    "- the `LLDF` class is used for data fusion\n",
-    "- the `LLDF_Settings` class is a helper class for setting up `LLDF`\n",
-    "- `LLDF` data can then be exported, or used for further processing"
+    "- the `DF` class is used for data fusion\n",
+    "- the `DFSettings` class is a helper class for setting up `DF`\n",
+    "- `DF` data can then be exported, or used for further processing"
    ]
   },
   {
@@ -106,11 +106,11 @@
     }
    ],
    "source": [
-    "from chemfusekit.lldf import LLDFSettings, LLDF, Table, GraphMode\n",
+    "from chemfusekit.df import DFSettings, DF, Table\n",
     "\n",
     "# Initialize the settings for low-level data fusion\n",
     "# Perform preliminary data fusion\n",
-    "lldf_settings = LLDFSettings(output=GraphMode.GRAPHIC)\n",
+    "df_settings = DFSettings(output='graphical')\n",
     "qepas_table = Table(\n",
     "    file_path=\"qepas.xlsx\",\n",
     "    sheet_name=\"Sheet1\",\n",
@@ -125,8 +125,8 @@
     "tables = [qepas_table, rt_table]\n",
     "\n",
     "# Initialize and run low-level data fusion\n",
-    "lldf = LLDF(lldf_settings, tables)\n",
-    "lldf.lldf()"
+    "df = DF(df_settings, tables)\n",
+    "df.fuse()"
    ]
   },
   {
@@ -136,7 +136,7 @@
    "outputs": [],
    "source": [
     "# (optional) export the LLDF data to an Excel file\n",
-    "lldf.export_data('output_file.xlsx')"
+    "df.export_data('output_file.xlsx')"
    ]
   },
   {
@@ -25582,19 +25582,19 @@
     }
    ],
    "source": [
-    "from chemfusekit.pca import PCASettings, PCA, GraphMode\n",
+    "from chemfusekit.pca import PCASettings, PCA\n",
     "\n",
     "# Initialize the settings for Principal Component Analysis\n",
     "pca_settings = PCASettings(\n",
     "    target_variance=0.99,\n",
     "    confidence_level=0.05,\n",
     "    initial_components=10,\n",
-    "    output=GraphMode.GRAPHIC # graphs will be printed as pictures\n",
+    "    output='graphical' # graphs will be printed as pictures\n",
     ")\n",
     "\n",
     "# Initialize and run the PCA class\n",
-    "pca = PCA(pca_settings, lldf.fused_data)\n",
-    "pca.pca()\n",
+    "pca = PCA(pca_settings, df.fused_data)\n",
+    "pca.train()\n",
     "\n",
     "# Print the number of components and the statistics\n",
     "print(f\"\\nNumber of components: {pca.components}\\n\")\n",
@@ -47180,16 +47180,16 @@
     }
    ],
    "source": [
-    "from chemfusekit.lda import LDASettings, LDA, GraphMode\n",
+    "from chemfusekit.lda import LDASettings, LDA\n",
     "\n",
     "settings = LDASettings(\n",
-    "    output=GraphMode.GRAPHIC,   # Graphs will be printed\n",
+    "    output='graphical',   # Graphs will be printed\n",
     "    test_split=True     # Run split test\n",
     ")\n",
     "\n",
     "# Initialize and run the LDA class\n",
     "lda = LDA(settings, pca_data)   # components will be determined automatically from the PCA data\n",
-    "lda.lda()"
+    "lda.train()"
    ]
   },
   {
@@ -47216,7 +47216,7 @@
    ],
    "source": [
     "# Let's pick a random sample and see if it gets recognized correctly:\n",
-    "x_data_sample = lldf.fused_data[119] # should be DMMP\n",
+    "x_data_sample = df.fused_data[119] # should be DMMP\n",
     "\n",
     "# Let's run the prediction:\n",
     "predictions = lda.predict(x_data_sample)\n",
diff --git a/examples/pca_lr_notebook.ipynb b/examples/pca_lr_notebook.ipynb
index 1dbf2e6..2938628 100644
--- a/examples/pca_lr_notebook.ipynb
+++ b/examples/pca_lr_notebook.ipynb
@@ -74,9 +74,9 @@
    "metadata": {},
    "source": [
     "## First step: Low-Level Data Fusion\n",
-    "- the `LLDF` class is used for data fusion\n",
-    "- the `LLDF_Settings` class is a helper class for setting up `LLDF`\n",
-    "- `LLDF` data can then be exported, or used for further processing"
+    "- the `DF` class is used for data fusion\n",
+    "- the `DFSettings` class is a helper class for setting up `DF`\n",
+    "- `DF` data can then be exported, or used for further processing"
    ]
   },
   {
@@ -106,11 +106,11 @@
     }
    ],
    "source": [
-    "from chemfusekit.lldf import LLDFSettings, LLDF, Table, GraphMode\n",
+    "from chemfusekit.df import DFSettings, DF, Table\n",
     "\n",
     "# Initialize the settings for low-level data fusion\n",
     "# Perform preliminary data fusion\n",
-    "lldf_settings = LLDFSettings(output=GraphMode.GRAPHIC)\n",
+    "df_settings = DFSettings(output='graphical')\n",
     "qepas_table = Table(\n",
     "    file_path=\"qepas.xlsx\",\n",
     "    sheet_name=\"Sheet1\",\n",
@@ -125,8 +125,8 @@
     "tables = [qepas_table, rt_table]\n",
     "\n",
     "# Initialize and run low-level data fusion\n",
-    "lldf = LLDF(lldf_settings, tables)\n",
-    "lldf.lldf()"
+    "df = DF(df_settings, tables)\n",
+    "df.fuse()"
    ]
   },
   {
@@ -136,7 +136,7 @@
    "outputs": [],
    "source": [
     "# (optional) export the LLDF data to an Excel file\n",
-    "lldf.export_data('output_file.xlsx')"
+    "df.export_data('output_file.xlsx')"
    ]
   },
   {
@@ -25582,19 +25582,19 @@
     }
    ],
    "source": [
-    "from chemfusekit.pca import PCASettings, PCA, GraphMode\n",
+    "from chemfusekit.pca import PCASettings, PCA\n",
     "\n",
     "# Initialize the settings for Principal Component Analysis\n",
     "pca_settings = PCASettings(\n",
     "    target_variance=0.99,\n",
     "    confidence_level=0.05,\n",
     "    initial_components=10,\n",
-    "    output=GraphMode.GRAPHIC    # graphs will be printed as pictures\n",
+    "    output='graphical'    # graphs will be printed as pictures\n",
     ")\n",
     "\n",
-    "# Initialize and run the PCA class\n",
-    "pca = PCA(pca_settings, lldf.fused_data)\n",
-    "pca.pca()\n",
+    "# Initialize and train the PCA class\n",
+    "pca = PCA(pca_settings, df.fused_data)\n",
+    "pca.train()\n",
     "\n",
     "# Print the number of components and the statistics\n",
     "print(f\"\\nNumber of components: {pca.components}\\n\")\n",
@@ -50119,20 +50119,20 @@
     }
    ],
    "source": [
-    "from chemfusekit.lr import LRSettings, LR, GraphMode\n",
+    "from chemfusekit.lr import LRSettings, LR\n",
     "\n",
     "settings = LRSettings(\n",
     "    algorithm='liblinear',\n",
-    "    output=GraphMode.GRAPHIC,   # graphs will be printed as pictures\n",
+    "    output='graphical',   # graphs will be printed as pictures\n",
     "    test_split=True # Run split test\n",
     ")\n",
     "\n",
-    "# Initialize and run the LDA class\n",
+    "# Initialize and train the LDA class\n",
     "lr = LR(\n",
     "    settings=settings,\n",
     "    data=pca_data   # use the data from PCA\n",
     ")\n",
-    "lr.lr()"
+    "lr.train()"
    ]
   },
   {
@@ -51045,10 +51045,8 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "# Let's pick a random sample and see if it gets recognized correctly:\n",
-    "reduced_dataset = pca.reduce(lldf.fused_data)   # Reduce the dimensionality of the samples\n",
+    "reduced_dataset = pca.reduce(df.fused_data)   # Reduce the dimensionality of the samples\n",
     "x_data_sample = reduced_dataset[119]    # Should be DMMP\n",
     "\n",
     "# Let's run the prediction:\n",
diff --git a/examples/plsda_notebook.ipynb b/examples/plsda_notebook.ipynb
index b1a0b52..3ba4c8f 100644
--- a/examples/plsda_notebook.ipynb
+++ b/examples/plsda_notebook.ipynb
@@ -74,9 +74,9 @@
    "metadata": {},
    "source": [
     "## First step: Low-Level Data Fusion\n",
-    "- the `LLDF` class is used for data fusion\n",
-    "- the `LLDF_Settings` class is a helper class for setting up `LLDF`\n",
-    "- `LLDF` data can then be exported, or used for further processing"
+    "- the `DF` class is used for data fusion\n",
+    "- the `DFSettings` class is a helper class for setting up `DF`\n",
+    "- `DF` data can then be exported, or used for further processing"
    ]
   },
   {
@@ -106,11 +106,11 @@
     }
    ],
    "source": [
-    "from chemfusekit.lldf import LLDFSettings, LLDF, Table, GraphMode\n",
+    "from chemfusekit.df import DFSettings, DF, Table\n",
     "\n",
     "# Initialize the settings for low-level data fusion\n",
     "# Perform preliminary data fusion\n",
-    "lldf_settings = LLDFSettings(output=GraphMode.GRAPHIC)\n",
+    "df_settings = DFSettings(output='graphical')\n",
     "qepas_table = Table(\n",
     "    file_path=\"qepas.xlsx\",\n",
     "    sheet_name=\"Sheet1\",\n",
@@ -125,8 +125,8 @@
     "tables = [qepas_table, rt_table]\n",
     "\n",
     "# Initialize and run low-level data fusion\n",
-    "lldf = LLDF(lldf_settings, tables)\n",
-    "lldf.lldf()"
+    "df = DF(df_settings, tables)\n",
+    "df.fuse()"
    ]
   },
   {
@@ -136,7 +136,7 @@
    "outputs": [],
    "source": [
     "# (optional) export the LLDF data to an Excel file\n",
-    "lldf.export_data('output_file.xlsx')"
+    "df.export_data('output_file.xlsx')"
    ]
   },
   {
@@ -18166,17 +18166,17 @@
     }
    ],
    "source": [
-    "from chemfusekit.plsda import PLSDASettings, PLSDA, GraphMode\n",
+    "from chemfusekit.plsda import PLSDASettings, PLSDA\n",
     "\n",
     "# Initialize the settings for Principal Component Analysis\n",
     "plsda_settings = PLSDASettings(\n",
     "    n_components=4, # Number of components\n",
-    "    output=GraphMode.GRAPHIC,   # graphs will be printed as pictures\n",
+    "    output='graphical',   # graphs will be printed as pictures\n",
     "    test_split=True # Split testing will be carried out\n",
     ")\n",
     "\n",
     "# Initialize and run the PLSDA class\n",
-    "plsda = PLSDA(settings=plsda_settings, data=lldf.fused_data)\n",
+    "plsda = PLSDA(settings=plsda_settings, data=df.fused_data)\n",
     "plsda.plsda()"
    ]
   },
@@ -18202,7 +18202,7 @@
    ],
    "source": [
     "# Let's pick a random sample and see if it gets recognized correctly:\n",
-    "x_data_sample = lldf.fused_data[119] # should be DMMP\n",
+    "x_data_sample = df.fused_data[119] # should be DMMP\n",
     "\n",
     "# Let's run the prediction:\n",
     "predictions = plsda.predict(x_data_sample)\n",
diff --git a/examples/svm_notebook.ipynb b/examples/svm_notebook.ipynb
index 394f84c..c6ac2b5 100644
--- a/examples/svm_notebook.ipynb
+++ b/examples/svm_notebook.ipynb
@@ -33,9 +33,9 @@
    "metadata": {},
    "source": [
     "## First step: Low-Level Data Fusion\n",
-    "- the `LLDF` class is used for data fusion\n",
-    "- the `LLDF_Settings` class is a helper class for setting up `LLDF`\n",
-    "- `LLDF` data can then be exported, or used for further processing"
+    "- the `DF` class is used for data fusion\n",
+    "- the `DFSettings` class is a helper class for setting up `DF`\n",
+    "- `DF` data can then be exported, or used for further processing"
    ]
   },
   {
@@ -44,11 +44,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from chemfusekit.lldf import LLDFSettings, LLDF, Table, GraphMode\n",
+    "from chemfusekit.df import DFSettings, DF, Table\n",
     "\n",
     "# Initialize the settings for low-level data fusion\n",
     "# Perform preliminary data fusion\n",
-    "lldf_settings = LLDFSettings(output=GraphMode.NONE)\n",
+    "df_settings = DFSettings(output='graphical')\n",
     "qepas_table = Table(\n",
     "    file_path=\"tests/qepas.xlsx\",\n",
     "    sheet_name=\"Sheet1\",\n",
@@ -63,8 +63,8 @@
     "tables = [qepas_table, rt_table]\n",
     "\n",
     "# Initialize and run low-level data fusion\n",
-    "lldf = LLDF(lldf_settings, tables)\n",
-    "lldf.lldf()"
+    "df = DF(df_settings, tables)\n",
+    "df.fuse()"
    ]
   },
   {
@@ -74,7 +74,7 @@
    "outputs": [],
    "source": [
     "# (optional) export the LLDF data to an Excel file\n",
-    "lldf.export_data('output_file.xlsx')"
+    "df.export_data('output_file.xlsx')"
    ]
   },
   {
@@ -2080,18 +2080,18 @@
     }
    ],
    "source": [
-    "from chemfusekit.svm import SVMSettings, SVM, GraphMode\n",
+    "from chemfusekit.svm import SVMSettings, SVM\n",
     "\n",
     "# Initialize the settings for Principal Component Analysis\n",
     "svm_settings = SVMSettings(\n",
     "    kernel='poly',    # Kernel type\n",
-    "    output=GraphMode.GRAPHIC,   # graphs will be printed as pictures\n",
+    "    output='graphical',   # graphs will be printed as pictures\n",
     "    test_split=True # Split testing will be carried out\n",
     ")\n",
     "\n",
-    "# Initialize and run the SVM class\n",
-    "svm = SVM(settings=svm_settings, data=lldf.fused_data)\n",
-    "svm.svm()"
+    "# Initialize and train the SVM class\n",
+    "svm = SVM(settings=svm_settings, data=df.fused_data)\n",
+    "svm.train()"
    ]
   },
   {
@@ -2116,7 +2116,7 @@
    ],
    "source": [
     "# Let's pick a random sample and see if it gets recognized correctly:\n",
-    "x_data_sample = lldf.fused_data.x_train.iloc[119] # should be DMMP\n",
+    "x_data_sample = df.fused_data.x_train.iloc[119] # should be DMMP\n",
     "x_data_sample = x_data_sample.iloc[1:].to_frame().transpose()\n",
     "\n",
     "# Let's run the prediction:\n",
diff --git a/tests/test_df.py b/tests/test_df.py
index b21dd9d..9efa379 100644
--- a/tests/test_df.py
+++ b/tests/test_df.py
@@ -1,6 +1,6 @@
 """This module contains the test cases for the LLDF module."""
 import unittest
-from chemfusekit.df import DFSettings, DF, GraphMode, Table
+from chemfusekit.df import DFSettings, DF, Table
 
 
 class TestLLDF(unittest.TestCase):
@@ -10,7 +10,7 @@ def test_file_loading(self):
         """Test case against file loading errors."""
         # load a non-existent file on purpose
         settings = DFSettings(
-            output=GraphMode.NONE
+            output='none'
         )
 
         table1 = Table(
@@ -27,7 +27,7 @@ def test_preprocessing_techniques(self):
         """Test case against wrong preprocessing user input."""
         with self.assertRaises(SyntaxError):
             settings = DFSettings(
-                output=GraphMode.NONE
+                output='none'
             )
 
             table1 = Table(
@@ -40,7 +40,7 @@ def test_preprocessing_techniques(self):
             df.fuse()
 
         # Now a correct value:
-        settings = DFSettings(output=GraphMode.NONE)
+        settings = DFSettings(output='none')
         table1 = Table(
             file_path='tests/qepas.xlsx',
             sheet_name='Sheet1',
@@ -51,7 +51,7 @@ def test_preprocessing_techniques(self):
 
     def test_export(self):
         """Test case against wrong export settings."""
-        settings = DFSettings(output=GraphMode.NONE)
+        settings = DFSettings(output='none')
         table1 = Table(
             file_path='tests/qepas.xlsx',
             sheet_name='Sheet1',
@@ -74,7 +74,7 @@ def test_midlevel_data_fusion(self):
         """Integration test case for mid-level data fusion."""
 
         # PCA
-        df_settings = DFSettings(output=GraphMode.GRAPHIC)
+        df_settings = DFSettings(output='graphical')
         table1 = Table(
             file_path='tests/qepas.xlsx',
             sheet_name='Sheet1',
@@ -93,7 +93,7 @@ def test_midlevel_data_fusion(self):
         df.fuse()
 
         # PLSDA
-        df_settings = DFSettings(output=GraphMode.GRAPHIC)
+        df_settings = DFSettings(output='graphical')
         table1 = Table(
             file_path='tests/qepas.xlsx',
             sheet_name='Sheet1',
diff --git a/tests/test_knn.py b/tests/test_knn.py
index 2916c89..6a95fe4 100644
--- a/tests/test_knn.py
+++ b/tests/test_knn.py
@@ -4,7 +4,7 @@
 import pandas as pd
 import numpy as np
 
-from chemfusekit.knn import KNNSettings, KNN, GraphMode
+from chemfusekit.knn import KNNSettings, KNN
 from chemfusekit.df import DFSettings, DF, DFDataModel, Table
 
 
@@ -61,12 +61,12 @@ def test_knn_settings(self):
 
         # output and test_split incompatibilities
         with self.assertRaises(Warning):
-            KNNSettings(output=GraphMode.NONE, test_split=True)
+            KNNSettings(output='none', test_split=True)
 
     def test_knn_constructor(self):
         """Test case against constructor errors."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -94,7 +94,7 @@ def test_knn_constructor(self):
     def test_knn(self):
         """Integration test case for the training function."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -114,19 +114,19 @@ def test_knn(self):
         knn.train()
 
         # With graph output
-        knn_settings = KNNSettings(output=GraphMode.GRAPHIC)
+        knn_settings = KNNSettings(output='graphical')
         knn = KNN(knn_settings, df.fused_data)
         knn.train()
 
         # With text output
-        knn_settings = KNNSettings(output=GraphMode.TEXT)
+        knn_settings = KNNSettings(output='text')
         knn = KNN(knn_settings, df.fused_data)
         knn.train()
 
     def test_prediction(self):
         """Test case against prediction parameter issues."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
diff --git a/tests/test_lda.py b/tests/test_lda.py
index 828465d..d192cf6 100644
--- a/tests/test_lda.py
+++ b/tests/test_lda.py
@@ -11,16 +11,16 @@ def test_lda_settings(self):
         """Test case against settings errors."""
         # Check for negative component rejection
         with self.assertRaises(ValueError):
-            LDASettings(components=-3, output=GraphMode.TEXT)
+            LDASettings(components=-3, output='text')
         # Check if split tests with no output cause warnings:
         with self.assertRaises(Warning):
-            LDASettings(output=GraphMode.NONE, test_split=True)
+            LDASettings(output='none', test_split=True)
 
     def test_lda_constructor(self):
         """Test case against constructor parameter issues."""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -54,7 +54,7 @@ def test_lda_constructor(self):
     def test_lda(self):
         """Integration test case."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -69,22 +69,22 @@ def test_lda(self):
         df.fuse()
 
         # Create an LDA object and train it, with graphical output
-        lda_settings = LDASettings(output=GraphMode.GRAPHIC)
+        lda_settings = LDASettings(output='graphical')
         lda = LDA(lda_settings, df.fused_data)
         lda.train()
 
         # Create an LDA object and train it, with text output
-        lda_settings = LDASettings(output=GraphMode.TEXT)
+        lda_settings = LDASettings(output='text')
         lda = LDA(lda_settings, df.fused_data)
         lda.train()
 
         # Create an LDA object and train it, with no output
-        lda_settings = LDASettings(output=GraphMode.NONE)
+        lda_settings = LDASettings(output='none')
         lda = LDA(lda_settings, df.fused_data)
         lda.train()
 
         # Create an LDA object and train it, with true output and split tests
-        lda_settings = LDASettings(output=GraphMode.TEXT, test_split=True)
+        lda_settings = LDASettings(output='text', test_split=True)
         lda = LDA(lda_settings, df.fused_data)
         lda.train()
 
@@ -92,7 +92,7 @@ def test_lda_predict(self):
         """Test case against prediction parameter issues."""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
diff --git a/tests/test_lr.py b/tests/test_lr.py
index c9024ea..69c56f6 100644
--- a/tests/test_lr.py
+++ b/tests/test_lr.py
@@ -6,7 +6,7 @@
 
 from chemfusekit.df import DFSettings, DF, Table
 from chemfusekit.pca import PCASettings, PCA
-from chemfusekit.lr import LRSettings, LR, GraphMode
+from chemfusekit.lr import LRSettings, LR
 from chemfusekit.__base import ReducerDataModel
 
 
@@ -19,7 +19,7 @@ def test_lr_settings(self):
         with self.assertRaises(ValueError):
             LRSettings(
                 algorithm='unknown',
-                output=GraphMode.NONE,
+                output='none',
                 test_split=False
             )
 
@@ -27,7 +27,7 @@ def test_lr_settings(self):
         with self.assertRaises(TypeError):
             LRSettings(
                 algorithm=None,
-                output=GraphMode.NONE,
+                output='none',
                 test_split=True
             )
         with self.assertRaises(TypeError):
@@ -39,18 +39,18 @@ def test_lr_settings(self):
         with self.assertRaises(TypeError):
             LRSettings(
                 algorithm='liblinear',
-                output=GraphMode.NONE,
+                output='none',
                 test_split=None
             )
 
         # Check if split tests with no output cause warnings:
         with self.assertRaises(Warning):
-            LRSettings(output=GraphMode.NONE, test_split=True)
+            LRSettings(output='none', test_split=True)
 
         # Should not raise any exception when the input is correct
         LRSettings(
             algorithm='liblinear',
-            output=GraphMode.TEXT
+            output='text'
         )
 
     def test_lr_constructor(self):
@@ -82,7 +82,7 @@ def test_lr_constructor(self):
     def test_lr(self):
         """Integration test case for LR training."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -109,23 +109,23 @@ def test_lr(self):
         lr.train()
 
         # With text output
-        lr_settings = LRSettings(output=GraphMode.TEXT)
+        lr_settings = LRSettings(output='text')
         lr = LR(lr_settings, pca_data)
         lr.train()
 
         # With graph output
         # With text output
-        lr_settings = LRSettings(output=GraphMode.GRAPHIC)
+        lr_settings = LRSettings(output='graphical')
         lr = LR(lr_settings, pca_data)
         lr.train()
 
         # With text output and split tests
-        lr_settings = LRSettings(output=GraphMode.TEXT, test_split=True)
+        lr_settings = LRSettings(output='text', test_split=True)
         lr = LR(lr_settings, pca_data)
         lr.train()
 
         # With graph output and split tests
-        lr_settings = LRSettings(output=GraphMode.GRAPHIC, test_split=True)
+        lr_settings = LRSettings(output='graphical', test_split=True)
         lr = LR(lr_settings, pca_data)
         lr.train()
 
diff --git a/tests/test_pca.py b/tests/test_pca.py
index aa35e4c..c929e47 100644
--- a/tests/test_pca.py
+++ b/tests/test_pca.py
@@ -1,7 +1,7 @@
 """This module contains the test cases for the PCA module."""
 import unittest
 import copy
-from chemfusekit.pca import PCASettings, PCA, GraphMode
+from chemfusekit.pca import PCASettings, PCA
 from chemfusekit.df import DFSettings, DF, Table
 from chemfusekit.lr import LRSettings, LR
 
@@ -28,14 +28,14 @@ def test_pca_settings(self):
             target_variance=0.98,
             confidence_level=0.9,
             initial_components=8,
-            output=GraphMode.GRAPHIC
+            output='graphical'
         )
 
     def test_pca_constructor(self):
         """Test case against constructor parameter issues."""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -72,7 +72,7 @@ def test_pca(self):
         whether the output is set to true or false
         """
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -87,7 +87,7 @@ def test_pca(self):
         df.fuse()
 
         # Set up and execute PCA (graph output)
-        pca_settings = PCASettings(output=GraphMode.GRAPHIC)
+        pca_settings = PCASettings(output='graphical')
         pca = PCA(pca_settings, df.fused_data)
         pca.train()
 
@@ -96,7 +96,7 @@ def test_pca(self):
         result_true_array_scores = copy.deepcopy(pca.array_scores)
 
         # Set up and execute PCA (again)
-        pca_settings = PCASettings(output=GraphMode.NONE)
+        pca_settings = PCASettings(output='none')
         pca = PCA(pca_settings, df.fused_data)
         pca.train()
 
@@ -108,7 +108,7 @@ def test_pca(self):
         self.assertEqual(result_true_array_scores, result_false_array_scores)
 
         # Set up and execute PCA (text output)
-        pca_settings = PCASettings(output=GraphMode.GRAPHIC)
+        pca_settings = PCASettings(output='graphical')
         pca = PCA(pca_settings, df.fused_data)
         pca.train()
 
@@ -117,7 +117,7 @@ def test_pca(self):
         result_true_array_scores = copy.deepcopy(pca.array_scores)
 
         # Set up and execute PCA (again)
-        pca_settings = PCASettings(output=GraphMode.NONE)
+        pca_settings = PCASettings(output='none')
         pca = PCA(pca_settings, df.fused_data)
         pca.train()
 
@@ -132,7 +132,7 @@ def test_pca_integration_lr(self):
         """Integration test for PCA+LR"""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -159,7 +159,7 @@ def test_pca_integration_lr(self):
     def test_pca_import_export(self):
         """Test case for the import and export of PCA models."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -198,7 +198,7 @@ def test_pca_import_export(self):
     def test_pca_reduce(self):
         """Test case for data dimensionality reduction."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
diff --git a/tests/test_plsda.py b/tests/test_plsda.py
index 1cfd525..ec34537 100644
--- a/tests/test_plsda.py
+++ b/tests/test_plsda.py
@@ -31,12 +31,12 @@ def test_plsda_settings(self):
 
         # output and test_split incompatibilities
         with self.assertRaises(Warning):
-            PLSDASettings(output=GraphMode.NONE, test_split=True)
+            PLSDASettings(output='none', test_split=True)
 
     def test_plsda_constructor(self):
         """Test case against constructor errors."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -64,7 +64,7 @@ def test_plsda_constructor(self):
     def test_plsda(self):
         """Integration test case for the training function."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -84,29 +84,29 @@ def test_plsda(self):
         plsda.train()
 
         # Set up and run PLSDA with text output
-        plsda_settings = PLSDASettings(output=GraphMode.TEXT)
+        plsda_settings = PLSDASettings(output='text')
         plsda = PLSDA(plsda_settings, df.fused_data)
         plsda.train()
 
         # Set up and run PLSDA with graphical output
-        plsda_settings = PLSDASettings(output=GraphMode.GRAPHIC)
+        plsda_settings = PLSDASettings(output='graphical')
         plsda = PLSDA(plsda_settings, df.fused_data)
         plsda.train()
 
         # Run with text output and split testing
-        plsda_settings = PLSDASettings(output=GraphMode.TEXT, test_split=True)
+        plsda_settings = PLSDASettings(output='text', test_split=True)
         plsda = PLSDA(plsda_settings, df.fused_data)
         plsda.train()
 
         # Run with graphical output and split testing
-        plsda_settings = PLSDASettings(output=GraphMode.GRAPHIC, test_split=True)
+        plsda_settings = PLSDASettings(output='graphical', test_split=True)
         plsda = PLSDA(plsda_settings, df.fused_data)
         plsda.train()
 
     def test_prediction(self):
         """Test case against prediction parameter issues."""
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
diff --git a/tests/test_svm.py b/tests/test_svm.py
index f1d9b5d..5e57f69 100644
--- a/tests/test_svm.py
+++ b/tests/test_svm.py
@@ -1,6 +1,6 @@
 """This module contains the test cases for the SVM module."""
 import unittest
-from chemfusekit.svm import SVMSettings, SVM, GraphMode
+from chemfusekit.svm import SVMSettings, SVM
 from chemfusekit.df import DFSettings, DF, Table
 
 
@@ -11,29 +11,29 @@ def test_svm_settings(self):
         """Test case against settings errors."""
         # Test against null type
         with self.assertRaises(TypeError):
-            SVMSettings(None, GraphMode.NONE, False)
+            SVMSettings(None, 'none', False)
         # Test against null output selector
         with self.assertRaises(TypeError):
             SVMSettings('linear', None, False)
         # Test against null test_split selector
         with self.assertRaises(TypeError):
-            SVMSettings('linear', GraphMode.NONE, None)
+            SVMSettings('linear', 'none', None)
         # Test against non-existent kernels
         with self.assertRaises(ValueError):
-            SVMSettings('non-existent', GraphMode.NONE, False)
+            SVMSettings('non-existent', 'none', False)
 
         # Check if split tests with no output cause warnings:
         with self.assertRaises(Warning):
-            SVMSettings(output=GraphMode.NONE, test_split=True)
+            SVMSettings(output='none', test_split=True)
 
         # Now call with proper values:
-        SVMSettings(kernel='gaussian', output=GraphMode.GRAPHIC, test_split=False)
+        SVMSettings(kernel='gaussian', output='graphical', test_split=False)
 
     def test_svm_constructor(self):
         """Test case against constructor parameter issues."""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -68,7 +68,7 @@ def test_svm(self):
         """Integration test case."""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",
@@ -83,17 +83,17 @@ def test_svm(self):
         df.fuse()
 
         # Create an SVM object and train it, with no output
-        svm_settings = SVMSettings(output=GraphMode.NONE)
+        svm_settings = SVMSettings(output='none')
         svm = SVM(svm_settings, df.fused_data)
         svm.train()
 
         # Create an SVM object and train it, with graphical output
-        svm_settings = SVMSettings(output=GraphMode.GRAPHIC)
+        svm_settings = SVMSettings(output='graphical')
         svm = SVM(svm_settings, df.fused_data)
         svm.train()
 
         # Create an SVM object and train it, with text output
-        svm_settings = SVMSettings(output=GraphMode.TEXT)
+        svm_settings = SVMSettings(output='text')
         svm = SVM(svm_settings, df.fused_data)
         svm.train()
 
@@ -101,7 +101,7 @@ def test_svm_predict(self):
         """Test case against prediction parameter issues."""
 
         # Perform preliminary data fusion
-        df_settings = DFSettings(output=GraphMode.NONE)
+        df_settings = DFSettings(output='none')
         table1 = Table(
             file_path="tests/qepas.xlsx",
             sheet_name="Sheet1",