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ML Introduction with scikit-learn

Cross-Validation ChallengeCross-Validation Challenge

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation.
The data is an already preprocessed Penguins dataset.
Some functions you will use:


Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3.
    Note: you can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

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Section 4. Chapter 5
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