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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>Module 4 More Analytics</title>
<meta charset="utf-8" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
<link rel="stylesheet" href="other.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Module 4 <br><br> More Analytics
---
<style type="text/css">
.remark-slide-content {
font-size: 30px;
padding: 1em 4em 1em 4em;
}
</style>
## Extensions to standard models
Nonlinear relationships
- Generalized Additive Models
- Nonlinear models (e.g. `\(y \sim a\cdot e^{b\cdot x}\)`)
Unsupervised methods
- Clustering/PCA etc.
---
## Extensions to standard models
Text Analysis
- Sentiment
- Tagging
- Topic modeling
- Word prediction
Penalized Regression
- Lasso, Ridge, Elasticnet
---
## Machine Learning
<span class="emph">Machine learning</span> is an **approach** to data analysis
- Focus is almost exclusively on prediction
- Performance is assessed on **new** data
Any method can be used for machine learning
Performance greater for ML, but interepretation, if even desired, can be more challenging
---
## Machine Learning
Standard methods
- Penalized regression (a starting point)
- Random forests and boosted trees (e.g. XGBoost)
- Neural nets
- Large scale graphical modeling (e.g. networks)
Standard techniques
- Data pre-processing
- Parameter tuning
- Cross-validation
- Combining models
---
## Machine Learning
A hypothetical decision tree
This would obviously misclassify many
<img src="img/tree.png" style="display:block; margin: 0 auto;" width=75%>
---
## Machine Learning
However, if we had a thousand trees...
... we could base our final results on the total output
- e.g. average (regression) or most common (classification) prediction
---
## Machine Learning
Popular R machine learning frameworks
- <span class="pack">caret</span>
- <span class="pack">mlr</span>
---
## Machine Learning
Data is typically homogenized to have similar inputs for all columns
```r
library(caret)
set.seed(1234) # so that the indices will be the same when re-run
trainIndices = createDataPartition(model_variables$libuser, p=.8, list=F)
X_train = model_variables %>%
slice(trainIndices)
X_test = model_variables %>%
slice(-trainIndices)
```
---
## Machine Learning
Example with XGBoost
```r
library(xgboost)
xgb_opts = expand.grid(
eta = c(.3, .4),
max_depth = c(9, 12),
colsample_bytree = c(.6, .8),
subsample = c(.5, .75, 1),
nrounds = 100, # 1000 would be more reasonable, but notably time consuming
min_child_weight = 1,
gamma = 0
)
cv_opts = trainControl(method='cv', number=10)
```
---
## Machine Learning
```r
# for parallel processing
library(doParallel)
cl = makeCluster(detectCores() - 1)
registerDoParallel(cl)
results_xgb = train(
libuser ~ .,
data = X_train,
method = 'xgbTree',
preProcess = c('center', 'scale'),
trControl = cv_opts,
tuneGrid = xgb_opts
)
stopCluster(cl)
results_xgb
```
---
## Machine Learning
```r
preds_gb = predict(results_xgb, X_test)
confusionMatrix(preds_gb, X_test$libuser, positive='yes')
```
```
Confusion Matrix and Statistics
Reference
Prediction no yes
no 3276 2271
yes 1726 2727
Accuracy : 0.6003
95% CI : (0.5906, 0.6099)
No Information Rate : 0.5002
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.2006
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.5456
Specificity : 0.6549
Pos Pred Value : 0.6124
Neg Pred Value : 0.5906
Prevalence : 0.4998
Detection Rate : 0.2727
Detection Prevalence : 0.4453
Balanced Accuracy : 0.6003
'Positive' Class : yes
```
---
## Machine Learning
Machine learning will take time to do well
- Need a baseline as a comparison
Payoff may be minimal compared to well-done standard methods that can handle data complexities
- Simply getting a good result is not enough
Many approaches are actually not very good with big data out of the box
---
## Deep Learning
Neural networks form the basis of <span class="emph">deep learning</span>
- <span class="emph">AI</span> refers to specific applications of of machine learning that employ <span class="emph">deep learning</span> techniques
<img src="img/nnet.png" style="display:block; margin: 0 auto;" width=50%>
---
## Deep Learning
With deep learning, there may be dozens of hidden layers with possibly hundreds of nodes and additional complexities
Requires GPU for reasonable times
Typically work best for a specific task with consistent data type
Common frameworks include <span class="pack">tensorflow</span>, <span class="pack">pytorch</span>, etc.
Common wrappers include <span class="pack">keras</span>, <span class="pack">fastai</span>, <span class="pack">scikit-learn</span>, etc.
---
## Network Analysis
.pull-left[
Graphs
- nodes and edges
- directed
- undirected
- bipartite
- weighted
Networks
- Social, biological, etc.
]
.pull-right[
![](https://upload.wikimedia.org/wikipedia/commons/9/9b/Social_Network_Analysis_Visualization.png)
<span style="font-size: 20%;">image courtesy of Wikipedia</span>
]
---
## Network Analysis
Description
- Degree, betweeness, centrality, pagerank
Clusters
- Community detection algorithms
Models
- Exponential random graph
- Bayesian networks
---
## Network Analysis
Demo
`network_analysis.R`
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