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.
Lösung
Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwendenDanke für Ihr Feedback!
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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.
Lösung
Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwendenDanke für Ihr Feedback!
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.
Lösung
Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwendenDanke für Ihr Feedback!
