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svm_ROC.py File Reference

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Namespaces

namespace  svm_ROC

Variables

list svm_ROC.all_tpr = []
tuple svm_ROC.classifier = svm.SVC(probability=True)
tuple svm_ROC.cv = StratifiedKFold(y, k=9)
 Code below modified from http://scikit-learn.org/stable/auto_examples/plot_roc_crossval.html#example-plot-roc-crossval-py.
list svm_ROC.data = svm_data['data']
tuple svm_ROC.data_scaled = scaler.transform(data)
string svm_ROC.label = 'Mean ROC (area = %0.2f)'
list svm_ROC.labels = svm_data['labels']
tuple svm_ROC.mean_auc = auc(mean_fpr, mean_tpr)
tuple svm_ROC.mean_fpr = np.linspace(0, 1, n_samples)
float svm_ROC.mean_tpr = 0.0
tuple svm_ROC.probas_ = classifier.fit(X[train], y[train])
tuple svm_ROC.roc_auc = auc(fpr, tpr)
tuple svm_ROC.scaler = pps.Scaler()
tuple svm_ROC.svm_data = pickle.load(f)


wouse
Author(s): Phillip M. Grice, Advisor: Prof. Charlie Kemp, Lab: The Healthcare Robotoics Lab at Georgia Tech.
autogenerated on Wed Nov 27 2013 11:57:42