svm_ROC Namespace Reference


list all_tpr = []
tuple classifier = svm.SVC(probability=True)
tuple cv = StratifiedKFold(y, k=9)
 Code below modified from
list data = svm_data['data']
tuple data_scaled = scaler.transform(data)
string label = 'Mean ROC (area = %0.2f)'
list labels = svm_data['labels']
tuple mean_auc = auc(mean_fpr, mean_tpr)
tuple mean_fpr = np.linspace(0, 1, n_samples)
float mean_tpr = 0.0
tuple probas_ =[train], y[train])
tuple roc_auc = auc(fpr, tpr)
tuple scaler = pps.Scaler()
tuple svm_data = pickle.load(f)

Variable Documentation

Definition at line 42 of file

tuple svm_ROC::classifier = svm.SVC(probability=True)

Definition at line 22 of file

tuple svm_ROC::cv = StratifiedKFold(y, k=9)

Code below modified from

Classification and ROC analysis Run classifier with crossvalidation and plot ROC curves

Definition at line 38 of file

Definition at line 15 of file

Definition at line 20 of file

string svm_ROC::label = 'Mean ROC (area = %0.2f)'

Definition at line 59 of file

Definition at line 14 of file

Definition at line 57 of file

tuple svm_ROC::mean_fpr = np.linspace(0, 1, n_samples)

Definition at line 41 of file

float svm_ROC::mean_tpr = 0.0

Definition at line 40 of file

Definition at line 45 of file

tuple svm_ROC::roc_auc = auc(fpr, tpr)

Definition at line 50 of file

tuple svm_ROC::scaler = pps.Scaler()

Definition at line 17 of file

tuple svm_ROC::svm_data = pickle.load(f)

Definition at line 13 of file

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