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- from sklearn.svm import LinearSVC
- from sklearn.calibration import CalibratedClassifierCV
- from sklearn import datasets
- #Load iris dataset
- iris = datasets.load_iris()
- X = iris.data[:, :2] # Using only two features
- y = iris.target #3 classes: 0, 1, 2
- linear_svc = LinearSVC() #The base estimator
- # This is the calibrated classifier which can give probabilistic classifier
- calibrated_svc = CalibratedClassifierCV(linear_svc,
- method='sigmoid', #sigmoid will use Platt's scaling. Refer to documentation for other methods.
- cv=3)
- calibrated_svc.fit(X, y)
- # predict
- prediction_data = [[2.3, 5],
- [4, 7]]
- predicted_probs = calibrated_svc.predict_proba(prediction_data) #important to use predict_proba
- print predicted_probs
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