Evaluate a brief explanation of ensemble learning

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Reference no: EM133482366

Objective: build understanding of Support Vector Machine classification

Assignment: Ensemble Learning with scikit-learn

Load the breast cancer dataset from sklearn.datasets (sklearn.datasets.load_breast_cancer - scikit-learn 1.2.2 documentation). Split the data into training and testing sets using a 70:30 ratio.
evaluate a Random Forest Classifier with the following parameters:

Number of estimators: 100
Maximum depth: 5

Train the Random Forest Classifier on the training data.
Evaluate the performance of the trained Random Forest Classifier on the testing data using accuracy as the evaluation metric.
Evaluate an AdaBoost Classifier with a Decision Tree base estimator with the following parameters:

Number of estimators: 50
Learning rate: 0.1

Train the AdaBoost Classifier on the training data.
Evaluate the performance of the trained AdaBoost Classifier on the testing data using accuracy as the evaluation metric.
Evaluate a Voting Classifier with the following ensemble methods:

Random Forest Classifier (from task 2)
AdaBoost Classifier (from task 5)

Compare the performance of the Random Forest Classifier and AdaBoost Classifier, and Voting Classifier. Which one performed the best?
evaluate a brief explanation of ensemble learning and its advantages in machine learning.

Reference no: EM133482366

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