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HOMEWORK
1
Introduction
2
Data understanding and preparation
2.1
Exploratory data analysis (EDA)
2.1.1
Missing values
2.1.2
Bad vs Good
2.1.3
Anomalies
2.1.4
Analysis of the clients
2.1.5
Boxplot - variable explanation
2.1.6
Correlation
3
Modeling
3.1
Data splitting and data balancing
3.2
Accuracy metric: training and fitting the models
3.2.1
Logistic regression
3.2.2
Nearest neighbour classification (KNN)
3.2.3
Support Vector Machine (SVM)
3.2.4
Neural network
3.2.5
Linear discriminant analysis (LDA)
3.2.6
Random Forest
3.2.7
Decision tree
3.2.8
Naive Bayes
3.2.9
Evaluation of the models
3.3
ROC metric: training and fitting the models
3.3.1
Evaluation of the models
4
DALEX
4.1
Training the models
4.2
Create an explainer
4.3
Dataset level
4.3.1
Model performance and model diagnostic
4.3.2
Model parts
4.3.3
Model profile
4.4
Instance level
4.4.1
Prediction parts
4.4.2
Prediction profile
4.5
Summary of DALEX results
5
Deployment and final words
References
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References
Burzykowski, Przemyslaw Biecek and Tomasz. 2020.
Explanatory
Model
Analysis
.
https://pbiecek.github.io/ema/
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