GridSearchCV
To improve model performance, we tune hyperparameters. The idea is simple: test different values, compute cross-validation scores, and choose the one with the highest score.
This process can be done using the GridSearchCV class of the sklearn.model_selection module.
GridSearchCV requires a model and a parameter grid (param_grid).
Example:
param_grid = {'n_neighbors': [1, 3, 5, 7]}
After initializing GridSearchCV, call .fit(X, y).
- The best model is in
.best_estimator_; - Its cross-validation score is in
.best_score_.
12345678910111213import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv') X, y = df.drop('species', axis=1), df['species'] param_grid = {'n_neighbors': [1,3,5,7,9]} grid_search = GridSearchCV(KNeighborsClassifier(), param_grid) grid_search.fit(X, y) print(grid_search.best_estimator_) print(grid_search.best_score_)
After fitting, GridSearchCV automatically retrains the best estimator on the full dataset.
The grid_search object becomes the final trained model and can be used directly with .predict() and .score().
12grid_search.fit(X, y) print(grid_search.score(X, y)) # training accuracy (not reliable for real evaluation)
Thanks for your feedback!
Ask AI
Ask AI
Ask anything or try one of the suggested questions to begin our chat
Can you explain what a parameter grid is and how to choose its values?
How does GridSearchCV perform cross-validation?
What does the best_estimator_ attribute represent?
Awesome!
Completion rate improved to 3.13GridSearchCV
Swipe to show menu
To improve model performance, we tune hyperparameters. The idea is simple: test different values, compute cross-validation scores, and choose the one with the highest score.
This process can be done using the GridSearchCV class of the sklearn.model_selection module.
GridSearchCV requires a model and a parameter grid (param_grid).
Example:
param_grid = {'n_neighbors': [1, 3, 5, 7]}
After initializing GridSearchCV, call .fit(X, y).
- The best model is in
.best_estimator_; - Its cross-validation score is in
.best_score_.
12345678910111213import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv') X, y = df.drop('species', axis=1), df['species'] param_grid = {'n_neighbors': [1,3,5,7,9]} grid_search = GridSearchCV(KNeighborsClassifier(), param_grid) grid_search.fit(X, y) print(grid_search.best_estimator_) print(grid_search.best_score_)
After fitting, GridSearchCV automatically retrains the best estimator on the full dataset.
The grid_search object becomes the final trained model and can be used directly with .predict() and .score().
12grid_search.fit(X, y) print(grid_search.score(X, y)) # training accuracy (not reliable for real evaluation)
Thanks for your feedback!
