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Oppiskele Challenge: Solving Task Using Stacking Classifier | Commonly Used Stacking Models
Ensemble Learning
course content

Kurssisisältö

Ensemble Learning

Ensemble Learning

1. Basic Principles of Building Ensemble Models
2. Commonly Used Bagging Models
3. Commonly Used Boosting Models
4. Commonly Used Stacking Models

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Challenge: Solving Task Using Stacking Classifier

Tehtävä

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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.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 4. Luku 2
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book
Challenge: Solving Task Using Stacking Classifier

Tehtävä

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.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 4. Luku 2
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
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