What is grid search in machine learning?

What is grid search in machine learning?

Grid search is a technique used to find the best hyperparameters for a machine learning model by exhaustively searching through a specified subset of hyperparameters.

How do I implement grid search in Python?

You can use the GridSearchCV class from the sklearn.model_selection module to implement grid search in Python.

Can you show me a code example?

Sure! Here's a simple example:

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

# Define the model
model = RandomForestClassifier()

# Define the parameter grid
param_grid = {'n_estimators': [10, 50, 100], 'max_depth': [None, 10, 20]}

# Set up GridSearchCV
grid_search = GridSearchCV(model, param_grid, cv=5)

# Fit the model
grid_search.fit(X_train, y_train)

This will search for the best combination of n_estimators and max_depth.

What should I consider when choosing hyperparameters?

Consider the model complexity, the size of your dataset, and the computational resources available, as these can affect performance and training time.