Random Search with RandomizedSearchCV
Random search quickly finds good hyperparameter settings by sampling random combinations instead of checking every possibility. This approach is especially efficient for models with many parameters, where grid search is too slow. In scikit-learn, RandomizedSearchCV automates random sampling and model evaluation for you.
Compare grid search vs. random search: grid search evaluates all possible combinations of the specified hyperparameters, which can be very slow as the number of parameters increases. In contrast, random search samples only a fixed number of random combinations, making it much faster in practice and often sufficient to find good results, especially when some hyperparameters are more important than others.
12345678910111213141516171819202122232425262728293031from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import RandomizedSearchCV from sklearn.datasets import load_iris from scipy.stats import randint # Load sample data X, y = load_iris(return_X_y=True) # Define the model rf = RandomForestClassifier(random_state=42) # Specify distributions for hyperparameters param_distributions = { 'n_estimators': randint(10, 200), 'max_depth': randint(2, 20) } # Set up RandomizedSearchCV random_search = RandomizedSearchCV( estimator=rf, param_distributions=param_distributions, n_iter=10, # Number of random combinations to try cv=3, random_state=42 ) # Fit to data random_search.fit(X, y) # Show best parameters print("Best parameters found:", random_search.best_params_)
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Random search quickly finds good hyperparameter settings by sampling random combinations instead of checking every possibility. This approach is especially efficient for models with many parameters, where grid search is too slow. In scikit-learn, RandomizedSearchCV automates random sampling and model evaluation for you.
Compare grid search vs. random search: grid search evaluates all possible combinations of the specified hyperparameters, which can be very slow as the number of parameters increases. In contrast, random search samples only a fixed number of random combinations, making it much faster in practice and often sufficient to find good results, especially when some hyperparameters are more important than others.
12345678910111213141516171819202122232425262728293031from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import RandomizedSearchCV from sklearn.datasets import load_iris from scipy.stats import randint # Load sample data X, y = load_iris(return_X_y=True) # Define the model rf = RandomForestClassifier(random_state=42) # Specify distributions for hyperparameters param_distributions = { 'n_estimators': randint(10, 200), 'max_depth': randint(2, 20) } # Set up RandomizedSearchCV random_search = RandomizedSearchCV( estimator=rf, param_distributions=param_distributions, n_iter=10, # Number of random combinations to try cv=3, random_state=42 ) # Fit to data random_search.fit(X, y) # Show best parameters print("Best parameters found:", random_search.best_params_)
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