Summary - Grid search

In this group you learned GridSearchCV and hyperparameter tuning.

Summary: grid search

In this group:

Hyperparameters are searched with CV, only on the training set; grids are built logarithmically.
GridSearchCV: Cartesian product, scoring, refit, n_jobs.
Conditional grids with a list of dicts; counting with ParameterGrid.
best_params_, best_estimator_, cv_results_; an optimum on the edge signals that the grid should be extended.
best_score_ is optimistic; RandomizedSearchCV for large searches.

Exercise Model selection in one search

For Pipeline([("olcek", StandardScaler()), ("model", SVC())]), try the "model" step among [SVC(), LogisticRegression(max_iter=5000), KNeighborsClassifier()] in the grid. Print the class name of the model chosen by GridSearchCV(..., cv=5) (type(gs.best_params_["model"]).__name__) and the test accuracy with round(..., 3) on separate lines.

Show example solution

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0, stratify=y)

pipe = Pipeline([("olcek", StandardScaler()), ("model", SVC())])
gs = GridSearchCV(pipe, {"model": [SVC(), LogisticRegression(max_iter=5000), KNeighborsClassifier()]}, cv=5)
gs.fit(X_train, y_train)
print(type(gs.best_params_["model"]).__name__)
print(round(gs.score(X_test, y_test), 3))

Test yourself

With the same budget, which is usually more efficient for searching a large 5-parameter space?
- A full GridSearchCV
- Manual trial
- Searching on the test set
- RandomizedSearchCV
Show answer

RandomizedSearchCV — Random search tries more distinct values of the important parameters.

Run the search on all cores.

gs = GridSearchCV(pipe, izgara, cv=5, ____=-1)

Show answer

gs = GridSearchCV(pipe, izgara, cv=5, n_jobs=-1)

Match the tools.

GridSearchCV

All combinations
RandomizedSearchCV

n_iter samples
ParameterGrid

List of combinations
cv_results_

Detailed results

Next lesson: What is SVM?

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