{"id":350,"date":"2020-10-05T06:48:24","date_gmt":"2020-10-05T06:48:24","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/10\/05\/robust-regression-for-machine-learning-in-python\/"},"modified":"2020-10-05T06:48:24","modified_gmt":"2020-10-05T06:48:24","slug":"robust-regression-for-machine-learning-in-python","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/10\/05\/robust-regression-for-machine-learning-in-python\/","title":{"rendered":"Robust Regression for Machine Learning in Python"},"content":{"rendered":"
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Regression is a modeling task that involves predicting a numerical value given an input.<\/p>\n

Algorithms used for regression tasks are also referred to as \u201cregression<\/em>\u201d algorithms, with the most widely known and perhaps most successful being linear regression.<\/p>\n

Linear regression fits a line or hyperplane that best describes the linear relationship between inputs and the target numeric value. If the data contains outlier values, the line can become biased, resulting in worse predictive performance. Robust regression<\/strong> refers to a suite of algorithms that are robust in the presence of outliers in training data.<\/p>\n

In this tutorial, you will discover robust regression algorithms for machine learning.<\/p>\n

After completing this tutorial, you will know:<\/p>\n