Robust Regression for Machine Learning in Python

Robust Regression for Machine Learning in Python

Robust Regression for Machine Learning in Python. In this tutorial, you will discover robust regression algorithms for machine learning.

Regression is a modeling task that involves predicting a numerical value given an input.

Algorithms used for regression tasks are also referred to as “_regression_” algorithms, with the most widely known and perhaps most successful being linear regression.

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 refers to a suite of algorithms that are robust in the presence of outliers in training data.

In this tutorial, you will discover robust regression algorithms for machine learning.

After completing this tutorial, you will know:

  • Robust regression algorithms can be used for data with outliers in the input or target values.
  • How to evaluate robust regression algorithms for a regression predictive modeling task.
  • How to compare robust regression algorithms using their line of best fit on the dataset.

Let’s get started.

python machine learning machine-learning

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