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Feature Engineering for ML Models

EasyMachine Learning00:00
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Problem

You are given a supervised learning problem where the raw inputs are not enough for the model to learn the pattern well. The question is how you would turn those inputs into better features, and why that changes model quality.

Context

inputs·Numerical usage counts, categorical plan metadata, timestamps, and sparse text tagstarget·Binary label for whether the user completes a key action within 7 daysdataset·200K user sessions, 38 columnsevaluation·AUC-ROC, F1, and lift at top decile

Question

Describe the process of feature engineering and its importance in machine learning.