Let AI build AI models for you.

What is AutoML?

Automatically find best model and hyperparameters.

**Goal**: Make ML accessible to non-experts!

Why AutoML?

**Speed**: Try hundreds of models automatically **Accuracy**: Often beats manual tuning **Efficiency**: Saves data scientist time

AutoML with Auto-sklearn

Install: ```bash pip install auto-sklearn ```

Use it: ```python import autosklearn.classification from sklearn.model_selection import train_test_split from sklearn.datasets import load_digits

Load data X, y = load_digits(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

AutoML automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, # 2 minutes per_run_time_limit=30 ) automl.fit(X_train, y_train)

Test score = automl.score(X_test, y_test) print(f"Accuracy: {score:.2f}")

See best model print(automl.show_models()) ```

AutoML with TPOT

```python from tpot import TPOTClassifier

AutoML with genetic programming tpot = TPOTClassifier( generations=5, population_size=20, verbosity=2, random_state=42 ) tpot.fit(X_train, y_train)

Test print(f"Score: {tpot.score(X_test, y_test):.2f}")

Export best pipeline tpot.export('best_model.py') ```

Popular AutoML Tools

**Auto-sklearn**: Based on scikit-learn **TPOT**: Genetic programming approach **H2O AutoML**: Enterprise solution **Google AutoML**: Cloud-based **AutoKeras**: For deep learning

When to Use AutoML

**Good for**: Quick prototypes, baseline models **Not ideal for**: Very specialized problems, when you need full control

Remember

- AutoML saves time on model selection - Still need good data and feature engineering - Great for getting started quickly - Understand the basics before using AutoML