Go to the source code of this file.
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) |