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svc_results.txt
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**************Support Vector Classifier*************************
******With extracted features and original********
Cross validation results:
Pipeline:
GridSearchCV(estimator=Pipeline(steps=[('Imputer',
SimpleImputer(strategy='median')),
('Standard scaler', StandardScaler()),
('Distance extractor', DistExtractor()),
('Standard scaler2', StandardScaler()),
('DF builder', DfBuilder()),
('svc',
SVC(probability=True,
random_state=42))]),
param_grid={'svc__C': (0.1, 0.5, 1.0, 10, 50),
'svc__gamma': ('scale', 'auto'),
'svc__kernel': ('linear', 'poly', 'rbf', 'sigmoid')})
Best param:
{'svc__C': 1.0, 'svc__gamma': 'scale', 'svc__kernel': 'poly'}
F1 avg cross val score: 0.9548387096774194
['b' 'm']
Classification report for TRAIN:
precision recall f1-score support
b 0.99 0.92 0.95 143
m 0.93 0.99 0.96 167
accuracy 0.96 310
macro avg 0.96 0.96 0.96 310
weighted avg 0.96 0.96 0.96 310
Classification report for TEST:
precision recall f1-score support
b 1.00 0.93 0.96 82
m 0.92 1.00 0.96 71
accuracy 0.96 153
macro avg 0.96 0.96 0.96 153
weighted avg 0.96 0.96 0.96 153
******With only extracted features********
Cross validation results:
Pipeline:
GridSearchCV(estimator=Pipeline(steps=[('Imputer',
SimpleImputer(strategy='median')),
('Standard scaler', StandardScaler()),
('Distance extractor',
DistExtractor(onlyCD=True)),
('scaler2', StandardScaler()),
('svc',
SVC(probability=True,
random_state=42))]),
param_grid={'svc__C': (0.1, 0.5, 1.0, 10, 50),
'svc__gamma': ('scale', 'auto'),
'svc__kernel': ('linear', 'poly', 'rbf', 'sigmoid')})
Best param:
{'svc__C': 0.1, 'svc__gamma': 'scale', 'svc__kernel': 'rbf'}
F1 avg cross val score: 0.9516129032258064
['b' 'm']
Classification report for TRAIN:
precision recall f1-score support
b 0.98 0.93 0.95 143
m 0.94 0.98 0.96 167
accuracy 0.96 310
macro avg 0.96 0.96 0.96 310
weighted avg 0.96 0.96 0.96 310
Classification report for TEST:
precision recall f1-score support
b 0.97 0.95 0.96 82
m 0.95 0.97 0.96 71
accuracy 0.96 153
macro avg 0.96 0.96 0.96 153
weighted avg 0.96 0.96 0.96 153
******Without extracted features********
Cross validation results:
Pipeline:
GridSearchCV(estimator=Pipeline(steps=[('Imputer',
SimpleImputer(strategy='median')),
('Standard scaler', StandardScaler()),
('svc',
SVC(probability=True,
random_state=42))]),
param_grid={'svc__C': (0.1, 0.5, 1.0, 10, 50),
'svc__gamma': ('scale', 'auto'),
'svc__kernel': ('linear', 'poly', 'rbf', 'sigmoid')})
Best param:
{'svc__C': 1.0, 'svc__gamma': 'scale', 'svc__kernel': 'poly'}
F1 avg cross val score: 0.9580645161290324
['b' 'm']
Classification report for TRAIN:
precision recall f1-score support
b 0.99 0.92 0.95 143
m 0.93 0.99 0.96 167
accuracy 0.96 310
macro avg 0.96 0.96 0.96 310
weighted avg 0.96 0.96 0.96 310
Classification report for TEST:
precision recall f1-score support
b 1.00 0.93 0.96 82
m 0.92 1.00 0.96 71
accuracy 0.96 153
macro avg 0.96 0.96 0.96 153
weighted avg 0.96 0.96 0.96 153