Open Machine Learning Course. Topic 5. Bagging and Random Forest

Yury Kashnitsky
Mar 5, 2018 · 15 min read
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Source: https://music.yandex.ua/artist/3177683

Article outline

1. Ensembles

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2. Bootstraping

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Service calls from loyal: mean interval [1.4077193 1.49473684] # Service calls from churn: mean interval [2.0621118 2.39761905]

3. Bagging

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4. Out-of-bag error

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5. Random Forest

Outline of part 5

5.1. Algorithm

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5.2. Comparison with Decision Trees and Bagging

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5.3. Parameters

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5.4. Extremely Randomized Trees

5.5. Transformation of a dataset into a high-dimensional representation

5.6. Pros and cons of Random Forests

6. Feature importance

6.1. Essence of the method

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6.2. Practical example

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7. Assignment #5

8. Useful resources


Open Machine Learning Course

A series of articles on basics of Machine Learning.

Yury Kashnitsky

Written by

Data Scientist at KPN, Netherlands, leader of mlcourse.ai

Open Machine Learning Course

A series of articles on basics of Machine Learning. Each article is followed by an assignment with a deadline. Several Kaggle Inclass competitions are held throughout the course.

Yury Kashnitsky

Written by

Data Scientist at KPN, Netherlands, leader of mlcourse.ai

Open Machine Learning Course

A series of articles on basics of Machine Learning. Each article is followed by an assignment with a deadline. Several Kaggle Inclass competitions are held throughout the course.