python-scikit-learnHow to use n_jobs for parallel training

The n_jobs parameter can be used to speed-up model training thanks to multiple parallel search jobs being executed:

from sklearn import datasets, neighbors, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = neighbors.KNeighborsClassifier(3, n_jobs=5)
model.fit(X_train, y_train)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`.KNeighborsClassifier(` K neighbors classification model
`n_jobs` number of parallel jobs to run for neighbors search (5 in our case)

Usage example

from sklearn import datasets, neighbors, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = neighbors.KNeighborsClassifier(3, n_jobs=5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(y_pred)

output

[1 2 1 0 2 1 1 2 2 1 1 2 2 2 2 0 2 0 0 0 0 2 2 2 0 0 1 1 0 0 1 2 0 2 1 2 1
 0]

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