What are the top hyperparameter tuning techniques for AI?

Grid search involves systematically testing a predefined set of hyperparameter combinations across a grid. It exhaustively evaluates all possible combinations to identify the one that yields the best model performance. Example: from sklearn.model_selection import GridSearchCV from sklearn.ensemble import RandomForestClassifier # Define hyperparameter grid param_grid = { 'n_estimators': [50, 100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4] } # Instantiate the model rf_classifier = RandomForestClassifier() # Perform grid search grid_search = GridSearchCV(estimator=rf_classifier, param_grid=param_grid, cv=5) grid_search.fit(X_train, y_train)

What are Hyperparameters: So there are many Hyperparameters like the number of layers, number of hidden units, learning rate decay, mini batch size etc.... these are external configurations which have to be set before training. Random selection if in some areas the points(i.e the Hyperparameters) which gives a better output, we can zoom into the particular region and add more points in that region so as to improvise the chances to get better results.

Random search randomly selects hyperparameter combinations for evaluation. This technique is more computationally efficient than grid search while still exploring a diverse set of hyperparameter values. Example: # Define hyperparameter distribution param_dist = { 'n_estimators': randint(50, 200), 'max_depth': [None, 10, 20, 30], 'min_samples_split': randint(2, 10), 'min_samples_leaf': randint(1, 5) } # Instantiate the model rf_classifier = RandomForestClassifier() # Perform random search random_search = RandomizedSearchCV(estimator=rf_classifier, param_distributions=param_dist, n_iter=10, cv=5) random_search.fit(X_train, y_train)

Bayesian optimization uses probabilistic models to predict the performance of different hyperparameter configurations. It actively explores the hyperparameter space by selecting promising configurations based on the model's predictions. Example: from skopt import BayesSearchCV from sklearn.ensemble import RandomForestClassifier # Define hyperparameter search space param_space = { 'n_estimators': (50, 200), 'max_depth': (None, 30), 'min_samples_split': (2, 10), 'min_samples_leaf': (1, 5) } # Instantiate the model rf_classifier = RandomForestClassifier() # Perform Bayesian optimization bayes_search = BayesSearchCV(estimator=rf_classifier, search_spaces=param_space, n_iter=30, cv=5) bayes_search.fit(X_train, y_train)

Evolutionary algorithms treat hyperparameter combinations as individuals in a population. They use genetic operators (mutation, crossover, selection) to evolve and optimize the population over multiple generations. Example: # Define hyperparameter search space param_space = { 'n_estimators': [50, 100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4] } # Instantiate the model rf_classifier = RandomForestClassifier() # Perform evolutionary search evo_search = EvolutionaryAlgorithmSearchCV(estimator=rf_classifier, params=param_space, scoring='accuracy', cv=5, population_size=10) evo_search.fit(X_train, y_train)

Ensemble Methods: - Combine predictions from multiple models with different hyperparameter settings to enhance robustness. - Example: Stacking or blending models with diverse hyperparameters. Parallelization: - Leverage parallel processing capabilities for faster HP tuning. - Example: Use parallel computing libraries or cloud-based services. Early Stopping: - Implement early stopping to halt training when model performance plateaus or starts degrading. - Example: Monitor validation performance and stop training if no improvement is observed. Domain Knowledge: - Incorporate domain knowledge to guide HP search and focus on values likely to be effective. - Example: Setting learning rates based on known characteristics of the problem.