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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) |