Feature Extraction Techniques in PySpark MLlib

Feature extraction is a crucial step in machine learning, where we transform raw data into a format that can be easily understood and processed by ML algorithms. It plays a vital role in ML because it helps to transform raw data into meaningful representations that capture the underlying patterns and characteristics of the data. These extracted features can then be used as input to ML models for training and prediction. PySpark MLlib provides a rich set of feature extraction techniques to handle diverse types of data.