Caret Package – A Practical Guide To Machine Learning In R Hk.saowen.com

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User Manual:

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Page Count: 24

• Caret Package – A Practical Guide to Machine Learning in R
  • Contents
  • 1. Introduction
  • 2. Initial Setup – load the package and dataset
  • 3. Data Preparation and Preprocessing
  • 3.1. How to split the dataset into training and validation?
  • 3.2 Descriptive statistics
  • 3.3 How to impute missing values using preProcess()?
  • 3.4 How to create One-Hot Encoding (dummy variables)?
  • 3.5 How to preprocess to transform the data?
  • 4. How to visualize the importance of variables using featurePlot()
  • 5. How to do feature selection using recursive feature elimination (rfe)?
  • 6. Training and Tuning the model
  • 6.1. How to train() the model and interpret the results?
  • 6.2 How to compute variable importance?
  • 6.3. Prepare the test dataset and predict
    • Missing Value imputation –> One-Hot Encoding –> Range Normalization
  • 6.4. Predict on testData
  • 6.5. Confusion Matrix
  • 7. How to do hyperparameter tuning to optimize the model for better performance?
  • 7.1. Setting up the trainControl()
  • 7.2 Hyper Parameter Tuning using tuneLength
  • 7.3. Hyper Parameter Tuning using tuneGrid
  • 8. How to evaluate performance of multiple machine learning algorithms?
  • 8.1. Training Adaboost
  • 8.2. Training Random Forest
  • 8.3. Training xgBoost Dart
  • 8.4. Training SVM
  • 8.5. Run resamples() to compare the models
  • 9. Ensembling the predictions
  • 9.1. How to ensemble predictions from multiple models using caretEnsemble?
  • 9.2. How to combine the predictions of multiple models to form a final prediction
  • 10. Conclusion