Automated Machine Learning (AutoML)

🎓 Components of AutoML

Data Preprocessing

Cleaning: Removing duplicates, missing values, outliers.

Encoding: One-hot, label encoding for categorical data.

Scaling: Normalization, standardization.

Automation: AutoML selects optimal transformations.

Feature Engineering

Creation: Polynomial features, interactions, transformations.

Selection: Choosing important features. Univariate, recursive elimination.

🔧 Hyperparameter Optimization

Bayesian Optimization

Concept: Intelligent search based on previous trials. Gaussian Process for modeling.

Benefits: Most efficient, fewer trials.

Tools: Optuna, Hyperopt, Scikit-optimize.

Evolutionary Algorithms

Concept: Evolutionary algorithms for searching. Mutations, crossover.

Benefits: Flexible, can find non-obvious solutions.

Drawbacks: Slower than Bayesian.

📚 Practical Examples

Example 1: Auto-sklearn for classification

Preparation: Prepare the data, split into train/test.

AutoML: Run Auto-sklearn with a time budget.

Evaluation: Check the leaderboard, select the best model.

Deployment: Use the best model for production.

Example 2: Optuna for hyperparameter optimization

Objective: Define an objective function (accuracy, F1).

Search Space: Define ranges for hyperparameters.

Optimization: Run Optuna for searching.

Best: Use the best hyperparameters.

Try it live

Everything above runs in your browser — open Automated Machine Learning (AutoML) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.