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Lære Challenge: Solving Task Using Stacking Classifier | Commonly Used Stacking Models
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Challenge: Solving Task Using Stacking Classifier

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The 'blood-transfusion-service-center' dataset is a dataset that contains information related to blood donation. It's often used as a binary classification task to predict whether a blood donor will donate blood again. The dataset includes several features that provide insights into the donor's history and characteristics.

Your task is to solve a classification task using the 'blood-transfusion-service-center' dataset:

  1. Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
  2. Use MLPClassifier as meta-model of an ensemble.
  3. Create a base_models list containing all base models of the ensemble.
  4. Finally, create a StackingClassifier model with specified base models and meta-model.

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Challenge: Solving Task Using Stacking Classifier

Opgave

Swipe to start coding

The 'blood-transfusion-service-center' dataset is a dataset that contains information related to blood donation. It's often used as a binary classification task to predict whether a blood donor will donate blood again. The dataset includes several features that provide insights into the donor's history and characteristics.

Your task is to solve a classification task using the 'blood-transfusion-service-center' dataset:

  1. Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
  2. Use MLPClassifier as meta-model of an ensemble.
  3. Create a base_models list containing all base models of the ensemble.
  4. Finally, create a StackingClassifier model with specified base models and meta-model.

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!

close

Awesome!

Completion rate improved to 4.55

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