Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Lernen Challenge: Evaluating the Model with Cross-Validation | Modeling
ML Introduction with scikit-learn
course content

Kursinhalt

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
3. Pipelines
4. Modeling

book
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:

Aufgabe

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.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. 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.
  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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 5
toggle bottom row

book
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:

Aufgabe

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.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. 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.
  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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 5
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
We're sorry to hear that something went wrong. What happened?
some-alt