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Learn Challenge: Creating a Complete ML Pipeline | Pipelines
ML Introduction with scikit-learn

bookChallenge: Creating a Complete ML Pipeline

Now create a pipeline that includes a final estimator. This produces a trained prediction pipeline that can generate predictions for new instances using the .predict() method.

Since a predictor requires the target variable y, encode it separately from the pipeline built for X. Use LabelEncoder to encode the target.

Note
Note

Since the predictions are encoded as 0, 1, or 2, the .inverse_transform() method of LabelEncoder can be used to convert them back to the original labels: 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

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Use the penguins dataset to build a pipeline with KNeighborsClassifier as the final estimator. Train the pipeline on the dataset and generate predictions for X.

  1. Encode the y variable.
  2. Create a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier.
  3. Use 'most_frequent' strategy with SimpleInputer.
  4. Train the pipe object using the features X and the target y.

Solution

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SectionΒ 3. ChapterΒ 6
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bookChallenge: Creating a Complete ML Pipeline

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Now create a pipeline that includes a final estimator. This produces a trained prediction pipeline that can generate predictions for new instances using the .predict() method.

Since a predictor requires the target variable y, encode it separately from the pipeline built for X. Use LabelEncoder to encode the target.

Note
Note

Since the predictions are encoded as 0, 1, or 2, the .inverse_transform() method of LabelEncoder can be used to convert them back to the original labels: 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

Swipe to start coding

Use the penguins dataset to build a pipeline with KNeighborsClassifier as the final estimator. Train the pipeline on the dataset and generate predictions for X.

  1. Encode the y variable.
  2. Create a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier.
  3. Use 'most_frequent' strategy with SimpleInputer.
  4. Train the pipe object using the features X and the target y.

Solution

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Everything was clear?

How can we improve it?

Thanks for your feedback!

close

Awesome!

Completion rate improved to 3.13
SectionΒ 3. ChapterΒ 6
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