Frequently Asked Questions(FAQs)

1.What is Featurization in machine learning?

Transforming raw data into numerical features that machine learning models can understand and process. This involves techniques like encoding, scaling, and normalization.

2.What is feature engineering for machine learning libraries?

Pre-built functions and tools within machine learning libraries designed to facilitate feature engineering tasks such as encoding, transformation, and selection.

3.What is feature engineering in EDA?

Applying feature engineering techniques during exploratory data analysis (EDA) to uncover hidden patterns, identify relationships between features, and understand the data distribution. This helps in selecting relevant features and building better models.

Feature Engineering is the process of creating new features or transforming existing features to improve the performance of a machine-learning model. It involves selecting relevant information from raw data and transforming it into a format that can be easily understood by a model. The goal is to improve model accuracy by providing more meaningful and relevant information.