Feature Selection, Feature Selection using scikit-learn and Feature Engineering

Feature Selection, Feature Selection using scikit-learn and Feature Engineering

Feature selection means selecting and retaining only the most important features in the model. Feature selection is different from feature extraction. In feature selection, we subset the features whereas in feature extraction, we create a new feature from the existing features.

Filter method is faster and useful when there are more number of features. Wrapper method gives better performance while the embedded method lies in between the other two methods.

Link to Feature Selection blogpost: https://www.datasciencesmachinelearning.com/2019/10/feature-selection-filter-method-wrapper.html

In this video, we will also understand how to perform Feature Selection using scikit-learn.

1) Dropping features which have low variance
A) Dropping features with zero variance
B) Dropping features with variance below the threshold variance
2) Univariate feature selection
3) Model based feature selection
4) Feature Selection using pipeline

Link to Feature Selection using scikit-learn blogpost: https://www.datasciencesmachinelearning.com/2019/10/feature-selection-using-sklearn.html

Feature Engineering for machine learning
Both Feature engineering and feature extraction are similar: both refer to creating new features from the existing features. Feature engineering refers to creating a new feature when we could have used the raw feature as well whereas feature extraction is creating new features when we can't use raw data in the analysis such as converting image to RGB values.

Link to Feature Engineering blogpost: https://www.datasciencesmachinelearning.com/2019/10/feature-engineering-and-feature.html
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