python-scikit-learnUsing OneClassSVM for outliers detection example

from sklearn import svm

X = [[0.32], [0.31], [0], [0.32], [0.31], [1], [0.32], [0.31]]

m = svm.OneClassSVM(gamma='auto').fit(X)
detected = m.predict(X)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`X =` example values set to detect outliers for
`.OneClassSVM(` creates unsupervised outlier detection model	`.fit(` train transformation model
`.predict(` predict target variable based on given features dataset	`detected` will contain a list of attributed outliers (`-1`) and values that are ok (`1`)

Usage example

from sklearn import svm

X = [[0.32], [0.31], [0], [0.32], [0.31], [1], [0.32], [0.31]]

m = svm.OneClassSVM(gamma='auto').fit(X)
detected = m.predict(X)

print(detected)

output

[ 1  1 -1  1  1 -1  1  1]

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