def bias_variance_analysis(clf_class, parameters, name): data_sizes = np.arange(60, 2000, 4) train_errors = [] test_errors = [] for data_size in data_sizes: train_error, test_error = measure( clf_class, parameters, name, data_size=data_size) train_errors.append(train_error) test_errors.append(test_error) plot_bias_variance(data_sizes, train_errors, test_errors, name, "Bias-Variance for '%s'" % name)
def bias_variance_analysis(clf_class, parameters, name): data_sizes = np.arange(60, 2000, 4) train_errors = [] test_errors = [] for data_size in data_sizes: train_error, test_error = measure( clf_class, parameters, name, data_size=data_size) train_errors.append(train_error) test_errors.append(test_error) plot_bias_variance(data_sizes, train_errors, test_errors, name, "Bias-Variance for '%s'" % name)
def bias_variance_analysis(clf_class, parameters, name,X,Y): data_sizes = np.arange(60, 2000, 4) train_errors = [] test_errors = [] for data_size in data_sizes: try: train_error, test_error,avg_sum = measure(clf_class, parameters, name, X,Y) train_errors.append(train_error) test_errors.append(test_error) except: print "data size error********************" print data_size plot_bias_variance(data_sizes, train_errors, test_errors, name, "Bias-Variance for '%s'" % name)