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ML Introduction with scikit-learn

bookChallenge: 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.

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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. Use 'most_frequent' strategy with SimpleInputer.
  4. Train the pipe object using the features X and the target y.

Lösning

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Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 6
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bookChallenge: 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.

Uppgift

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. Use 'most_frequent' strategy with SimpleInputer.
  4. Train the pipe object using the features X and the target y.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 6
single

single

close

Awesome!

Completion rate improved to 3.13

bookChallenge: Creating a Complete ML Pipeline

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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.

Uppgift

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. Use 'most_frequent' strategy with SimpleInputer.
  4. Train the pipe object using the features X and the target y.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

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