Grid search in Python

GridSearchCV tries every combination with cross validation and keeps the best.

from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

X, y = load_iris(return_X_y=True)
pipeline = Pipeline([("scale", StandardScaler()), ("svc", SVC())])

grid = {
    "svc__C": [0.1, 1.0, 10.0],
    "svc__kernel": ["linear", "rbf"],
}
search = GridSearchCV(pipeline, grid, cv=5, scoring="accuracy", n_jobs=1)
search.fit(X, y)

print(search.best_params_)
print(f"{search.best_score_:.3f}")
print(search.best_estimator_.named_steps["svc"].C)

How it works

1. Parameter names use step__parameter inside a pipeline.
2. best_params_ and best_score_ report the winner.
3. refit=True leaves the best model fitted on all the data.

Keywords and builtins used here

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Step 3 of 4 in Validation, step 10 of 25 in Machine learning with scikit-learn.

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