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
- 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