AutoML (Automated Machine Learning)

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:

pip install auto-sklearn

Use it:

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

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