November 2017
Intermediate to advanced
374 pages
10h 19m
English
Content preview from scikit-learn Cookbook , Second Edition - Second Edition
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How it works...
The TheilSenRegressor is a robust estimator that performs well in the presence of outliers. It uses the measurement of medians, which is more robust to outliers. In OLS regression, errors are squared, and thus a squared error can decrease good results.
You can try several robust estimators in scikit-learn Version 0.19.0:
from sklearn.linear_model import Ridge, LinearRegression, TheilSenRegressor, RANSACRegressor, ElasticNet, HuberRegressorfrom sklearn.metrics import r2_score, mean_absolute_errornamed_estimators = [('OLS ', LinearRegression()),('Ridge ', Ridge()),('TSR ', TheilSenRegressor()),('RANSAC', RANSACRegressor()),('ENet ',ElasticNet()),('Huber ',HuberRegressor())]for num_index, est in enumerate(named_estimators):Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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