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  3. Machine Learning with caret in R
  • 1

    Regression Models: Fitting and Evaluating Their Performance

    Gratuito

    In the first chapter of this course, you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).

  • 2

    Classification Models: Fitting and Evaluating Their Performance

    In this chapter, you'll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).

  • 3

    Tuning Model Parameters to Improve Performance

    In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.

  • 4

    Preprocessing Data

    In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.

  • 5

    Selecting Models: A Case Study in Churn Prediction

    In the final chapter of this course, you'll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).

Initializing

Exercicio

Make a custom trainControl

The wine quality dataset was a regression problem, but now you are looking at a classification problem. This is a simulated dataset based on the "don't overfit" competition on Kaggle a number of years ago.

Classification problems are a little more complicated than regression problems because you have to provide a custom summaryFunction to the train() function to use the AUC metric to rank your models. Start by making a custom trainControl, as you did in the previous chapter. Be sure to set classProbs = TRUE, otherwise the twoClassSummary for summaryFunction will break.

Instruções

100 XP

Make a custom trainControl called myControl for classification using the trainControl function.

  • Use 10 CV folds.
  • Use twoClassSummary for the summaryFunction.
  • Be sure to set classProbs = TRUE.