Applied Data Science Coding in Python: Feature Extraction with Univariate Statistics

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Applied Data Science Coding in Python: Feature Extraction with Univariate Statistics

“Feature Extraction with Univariate Statistics” is a technique used in Python to select the most informative features from a dataset. It is based on the statistical tests for the presence of a relationship between each feature and the target variable. The feature selection is based on the statistical test values of each feature and the target variable.

The process starts by calculating the univariate statistical test score of each feature. The scores are then used to rank the features based on their importance. The top-ranking features are then selected and used in the model.

Univariate feature selection can be used with various models such as linear regression, decision trees, and support vector machines. It is a simple and fast method to select features, but it only considers the relationship between each feature and the target variable independently. In contrast, other feature selection methods such as mutual information, consider the mutual relationship between features.

Univariate feature selection is useful for identifying the most important features in a dataset which in turn can be used for further analysis and can also help in reducing the dimensionality of a dataset, which can improve the performance of a model and make it easier to interpret.

 

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



Applied Data Science Coding in Python: Feature Extraction with Univariate Statistics

 

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