Why do we extract features?

Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features) . These new reduced set of features should then be able to summarize most of the information contained in the original set of features.

When plotted, many time series exhibit one or more of the following features:

Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set . It yields better results than applying machine learning directly to the raw data.

Why Feature Extraction is Useful? The technique of extracting the features is useful when you have a large data set and need to reduce the number of resources without losing any important or relevant information . Feature extraction helps to reduce the amount of redundant data from the data set.