Ai Hyperparameter Tuning — Free AI & ML Tutorial

TL;DR: Learn Ai Hyperparameter Tuning in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.

Ai Hyperparameter Tuning in AI & ML

Hyperparameter tuning optimizes model parameters (learning rate, tree depth, regularization) that control learning behavior.

Grid search exhaustively evaluates all combinations of hyperparameters; computationally expensive but thorough.

Random search samples random combinations, often more efficient than grid search for high-dimensional spaces.

Bayesian optimization models the hyperparameter space as a probabilistic function, intelligently selecting next trials.

Automated Machine Learning (AutoML) automatically selects algorithms, hyperparameters, and feature engineering steps.

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline

# Grid Search: exhaustive over specified parameters
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [5, 10, 15, None],
    'min_samples_split': [2, 5, 10],
}
grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train, y_train)

# Random Search: sample random combinations
param_dist = {
    'n_estimators': [50, 100, 200, 300],
    'max_depth': range(3, 20),
}
random_search = RandomizedSearchCV(RandomForestClassifier(), param_dist, n_iter=10, cv=5, random_state=42)
random_search.fit(X_train, y_train)

print(f"Best params: {grid_search.best_params_}")
print(f"Best CV score: {grid_search.best_score_:.4f}")
    

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