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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
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from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(
digits.data, digits.target)
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from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
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cross_val_score(KNeighborsClassifier(),
X_train, y_train, cv=5)
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from sklearn.model_selection import KFold, StratifiedKFold
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cross_val_score(KNeighborsClassifier(),
X_train, y_train, cv=KFold(n_splits=10, shuffle=True, random_state=42))
Grid-Search with build-in cross validation
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from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
Define parameter grid:
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import numpy as np
param_grid = {'C': 10. ** np.arange(-3, 3),
'gamma' : 10. ** np.arange(-5, 0)}
np.set_printoptions(suppress=True)
print(param_grid)
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grid_search = GridSearchCV(SVC(), param_grid, verbose=3, cv=5)
A GridSearchCV object behaves just like a normal classifier.
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grid_search.fit(X_train, y_train)
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grid_search.predict(X_test)
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grid_search.score(X_test, y_test)
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grid_search.best_params_
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grid_search.best_score_
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grid_search.best_estimator_
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# We extract just the scores
scores = grid_search.cv_results_['mean_test_score']
scores = np.array(scores).reshape(6, 5)
plt.matshow(scores)
plt.xlabel('gamma')
plt.ylabel('C')
plt.colorbar()
plt.xticks(np.arange(5), param_grid['gamma'])
plt.yticks(np.arange(6), param_grid['C']);
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# %load solutions/grid_search_k_neighbors.py