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Leer Challenge: Solving Task Using Bagging Classifier | Commonly Used Bagging Models
Ensemble Learning
course content

Cursusinhoud

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 Bagging Classifier

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The load_breast_cancer dataset is a built-in dataset provided by scikit-learn. It is commonly used for binary classification tasks, particularly in the context of breast cancer diagnosis. This dataset contains features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The aim is to predict whether a given mass is malignant (cancerous) or benign (non-cancerous).

Your task is to solve the classification problem using BaggingClassifier on load_breast_cancer dataset:

  1. Create an instance of BaggingClassifier class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to 10.
  2. Fit the ensemble model.
  3. Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.

Oplossing

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Sectie 2. Hoofdstuk 2
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book
Challenge: Solving Task Using Bagging Classifier

Taak

Swipe to start coding

The load_breast_cancer dataset is a built-in dataset provided by scikit-learn. It is commonly used for binary classification tasks, particularly in the context of breast cancer diagnosis. This dataset contains features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The aim is to predict whether a given mass is malignant (cancerous) or benign (non-cancerous).

Your task is to solve the classification problem using BaggingClassifier on load_breast_cancer dataset:

  1. Create an instance of BaggingClassifier class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to 10.
  2. Fit the ensemble model.
  3. Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 2. Hoofdstuk 2
Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
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