Overview
TPOT is a Python Automated Machine Learning (AutoML) tool that utilizes genetic programming to optimize machine learning pipelines. It automates the process of selecting and configuring machine learning algorithms, preprocessing steps, and feature engineering techniques. TPOT explores various combinations of these components to identify the pipeline that maximizes predictive performance for a given dataset. The architecture involves creating a population of pipeline configurations, evaluating their performance using cross-validation, and evolving the population through genetic operators like mutation and crossover. TPOT is designed to simplify the process of building effective machine learning models, especially for users with limited experience in algorithm selection and hyperparameter tuning. It supports classification and regression tasks and integrates with scikit-learn, providing a seamless experience for Python users.