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Lære Challenge: Creating a Complete ML Pipeline | Pipelines
ML Introduction with scikit-learn
course content

Kursusindhold

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: Creating a Complete ML Pipeline

Now let's create a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.

To train a predictor (model), you need y to be encoded. This is done separately from the pipeline we build for X. Remember that LabelEncoder is used for encoding the target.

Opgave

Swipe to start coding

You have the same penguins dataset. The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.

  1. Encode the y variable.
  2. Create a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier.
  3. Train the pipe object using the features X and the target y.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 6
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book
Challenge: Creating a Complete ML Pipeline

Now let's create a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.

To train a predictor (model), you need y to be encoded. This is done separately from the pipeline we build for X. Remember that LabelEncoder is used for encoding the target.

Opgave

Swipe to start coding

You have the same penguins dataset. The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.

  1. Encode the y variable.
  2. Create a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier.
  3. Train the pipe object using the features X and the target y.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 6
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Vi beklager, at noget gik galt. Hvad skete der?
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