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 + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
model_selection.train_test_splitsplits given | .KNeighborsClassifier(K neighbors classification model |
n_jobsnumber 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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