Introduction to Ensemble Learning and Bagging
Introduction to Ensemble Learning and Bagging Data Science Project
Classification in Depth with Scikit-Learn

Introduction to Ensemble Learning and Bagging

This project focuses on ensemble learning, a popular technique in machine learning that involves combining multiple models to improve performance. You will gain practical experience by implementing and training bagging models and understanding their foundations. By the end of the project, you will have gained a solid understanding of bagging and how it can be used to improve the performance of machine learning models.
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Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

multiplechoice

Check null values

How many null values are present in the dataset?

There could be more than just one correct answer.

multiplechoice

Transform RainTomorrow

Choice the correct code to transform RainTomorrow to a numeric variable using map.

multiplechoice

Split the dataset

Select the correct way to split the dataset into 30% test and 70% train.

There could be more than just one correct answer.

input

Evaluate the accuracy of the ensemble.

Based on previous predictions compute the accuracy score.

Round to two decimal places

multiplechoice

Evaluate the accuracy

Evaluate the accuracy of the training and testing dataset

multiplechoice

Decision boundaries

After visualizing the decision boundaries. Answer the following question.

Do you have to normalize the data?

input

OOB score

For this activity train a BaggingClassifier using only two features: ['MaxTemp', 'Humidity3pm'], a random_state of 42, and compute the oob_score.

Round to two decimal places

Introduction to Ensemble Learning and BaggingIntroduction to Ensemble Learning and Bagging
Author

Verónica Barraza

This project is part of

Classification in Depth with Scikit-Learn

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