# Plot learning curve (with costs) costs = np.squeeze(d['costs']) plt.plot(costs) plt.ylabel('cost') plt.xlabel('iterations (per hundreds)') plt.title("Learning rate =" + str(d["learning_rate"])) plt.show() 画散点图:plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap...
probArray = numpy.asarray(probList) fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @input_data_1 =...
probArray = numpy.asarray(probList) fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @i...
probArray = numpy.asarray(probList) fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @input_data_1 = @...
fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ',