D. Modeling

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• D.1- Data Transformation and Encoding
  • D.1.1- Transformation (Scaling Numerical Variables)
  • D.1.2- Encoding Categorical Variables (Classic/Contrast/Bayesian)
• D.2. Metric, Baseline and Estimator/Classifier Selection
  • D.2.1- Metric and Baseline Selection for Model Evaluation
    • D.2.1.1- Metric Selection
    • D.2.1.2- Baseline Selection
• D.3- Estimator/Classifier Selection
  • D.3.1- K-Nearest Neighbors (KNN)
  • D.3.2- Naive Bayes Classifier
  • D.3.3- Logistic Regression Classifier
  • D.3.4- Support Vector Machines Classifier
  • D.3.5- Neural Network Classifiers
    • D.3.5.1- Multi-Layer Perceptron (MLP)
    • D.3.5.2- Convolutional Neural Networks (CNN)
    • D.3.5.3- Recurrent Neural Networks (RNN)
  • D.3.6- Ensembled Classifiers
    • D.3.6.1- Random Forest Classifier (Bagging)
    • D.3.6.2- Gradient Boosting Classifier
    • D.3.6.3- Stacked Generalization (Stacking)
• D.4- Model Selection (Hyperparameter Tuning)