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Machine Learning - Grid Search
Grid Search is a hyperparameter tuning technique in Machine Learning that helps to find the best combination of hyperparameters for a given model. It works by defining a grid of hyperparameters and then training the model with all the possible combinations of hyperparameters to find the best performing set.
In other words, Grid Search is an exhaustive search method where a set of hyperparameters are defined, and a search is performed over all possible combinations of these hyperparameters to find the optimal values that give the best performance.
Implementation in Python
In Python, Grid Search can be implemented using the GridSearchCV class from the sklearn module. The GridSearchCV class takes the model, the hyperparameters to tune, and a scoring function as input. It then performs an exhaustive search over all possible combinations of hyperparameters and returns the best set of hyperparameters that give the best score.
Here is an example implementation of Grid Search in Python using the GridSearchCV class −
Example
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
# Generate a sample dataset
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2)
# Define the model and the hyperparameters to tune
model = RandomForestClassifier()
hyperparameters = {'n_estimators': [10, 50, 100], 'max_depth': [None, 5, 10]}
# Define the Grid Search object and fit the data
grid_search = GridSearchCV(model, hyperparameters, scoring='accuracy', cv=5)
grid_search.fit(X, y)
# Print the best hyperparameters and the corresponding score
print("Best hyperparameters: ", grid_search.best_params_)
print("Best score: ", grid_search.best_score_)
In this example, we define a RandomForestClassifier model and a set of hyperparameters to tune, namely the number of trees (n_estimators) and the maximum depth of each tree (max_depth). We then create a GridSearchCV object and fit the data using the fit() method. Finally, we print the best set of hyperparameters and the corresponding score.
Output
When you execute this code, it will produce the following output −
Best hyperparameters: {'max_depth': None, 'n_estimators': 10}
Best score: 0.953