Applied Data Science Coding in Python: Feature Extraction with RFE

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Applied Data Science Coding in Python: Feature Extraction with RFE

“Feature Extraction with RFE” is a technique used in Python to select the most informative features from a dataset. RFE stands for “Recursive Feature Elimination” and it is a method used to select the most relevant features from a dataset by recursively eliminating less important features.

The process starts by fitting a model to the data, and then ranking the features based on their importance. The least important feature is then removed and the process is repeated until a desired number of features is reached. The final set of features is then used in the model.

RFE can be used with various models such as linear regression, decision trees, and support vector machines. It is a useful technique for reducing the dimensionality of a dataset, which can improve the performance of a model and make it easier to interpret. It also helps in identifying the most important features in a dataset which in turn can be used for further analysis.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: Feature Extraction with RFE.



Applied Data Science Coding in Python: Feature Extraction with RFE

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