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.
Lösning
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedanTack för dina kommentarer!
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Completion rate improved to 3.13Awesome!
Completion rate improved to 3.13Challenge: 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.
Lösning
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedanTack för dina kommentarer!
single
Awesome!
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.
Lösning
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedanTack för dina kommentarer!
