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Challenge: Solving Task Using AdaBoost Classifier | Commonly Used Boosting Models
Ensemble Learning

Challenge: Solving Task Using AdaBoost Classifier

Task

The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.

Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:

  1. Split data into train and test sets.
  2. Use the AdaBoostClassifier() constructor to create the model with 50 base estimators.

    Note

    If we don't specify the base model of AdaBoostClassifer, the Decision Tree Classifier will be used by default.

Everything was clear?

Section 3. Chapter 2
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Course Content

Ensemble Learning

Challenge: Solving Task Using AdaBoost Classifier

Task

The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.

Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:

  1. Split data into train and test sets.
  2. Use the AdaBoostClassifier() constructor to create the model with 50 base estimators.

    Note

    If we don't specify the base model of AdaBoostClassifer, the Decision Tree Classifier will be used by default.

Everything was clear?

Section 3. Chapter 2
toggle bottom row
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