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Python 3
Hyperparameter Tuning Using GridSearchCV

Hyperparameter Tuning Using GridSearchCV

Hyperparameter Tuning Using GridSearchCV

Hyperparameter Tuning Using GridSearchCV

Instead of selecting the number of neighbors manually, use GridSearchCV to identify the optimal value.

Code

parameters = {
'n_neighbors': range(2, 51)
}
grid = GridSearchCV(
KNeighborsRegressor(),
parameters,
cv=5
)
grid.fit(
X_train,
Y_train
)
print(grid.best_params_)

Explanation

GridSearchCV automatically evaluates multiple values of K using cross-validation and selects the value that produces the best performance.

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Hyperparameter Tuning Using GridSearchCV

Contents

Hyperparameter Tuning Using GridSearchCV

Code

Explanation

Stock Price Prediction for Beginners using Data Science

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