April 2019
Intermediate to advanced
426 pages
11h 13m
English
Content preview from Mastering Python for Finance - Second Edition
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Random forests regression
Random forests consist of multiple decision trees each based on a random sub-sample of the training data and uses averaging to improve the predictive accuracy and to control overfitting. Selection by random inadvertently introduces some form of bias. However, due to averaging, it variance also decreases, helping to compensate for the increase in bias, and is considered to yield an overall better model.
The sklearn.ensemble module provides a random forest regressor called RandomForestRegressor.
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