Challenge: Evaluating the Model with Cross-Validation
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.
Here are some of the functions you will use:
Swipe to start coding
Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.
- Initialize a
KNeighborsClassifierwith 4 neighbors. - Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a
cross_val_score()function. - Use a suitable function to split
X, y. - Train the model using the training set.
- Evaluate the model using the test set.
Løsning
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Completion rate improved to 3.13Challenge: Evaluating the Model with Cross-Validation
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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.
Here are some of the functions you will use:
Swipe to start coding
Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.
- Initialize a
KNeighborsClassifierwith 4 neighbors. - Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a
cross_val_score()function. - Use a suitable function to split
X, y. - Train the model using the training set.
- Evaluate the model using the test set.
Løsning
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Awesome!
Completion rate improved to 3.13single
