我有10多个功能和十几个案例来训练逻辑回归以对人类进行分类。第一个例子是法语和非法语,第二个例子是英语和非英语。结果如下:
//////////////////////////////////////////////////////
1= fr
0= non-fr
Class count:
0 69109
1 30891
dtype: int64
Accuracy: 0.95126
Classification report:
precision recall f1-score support
0 0.97 0.96 0.96 34547
1 0.92 0.93 0.92 15453
avg / total 0.95 0.95 0.95 50000
Confusion matrix:
[[33229 1318]
[ 1119 14334]]
AUC= 0.944717975754
//////////////////////////////////////////////////////
1= en
0= non-en
Class count:
0 76125
1 23875
dtype: int64
Accuracy: 0.7675
Classification report:
precision recall f1-score support
0 0.91 0.78 0.84 38245
1 0.50 0.74 0.60 11755
avg / total 0.81 0.77 0.78 50000
Confusion matrix:
[[29677 8568]
[ 3057 8698]]
AUC= 0.757955582999
//////////////////////////////////////////////////////
然而,我得到一些非常奇怪的AUC曲线,具有三角形而不是锯齿状的圆形曲线。关于为什么我会这样形状的任何解释?我犯过任何可能的错误?
代码:
all_dict = []
for i in range(0, len(my_dict)):
temp_dict = dict(my_dict[i].items() + my_dict2[i].items() + my_dict3[i].items() + my_dict4[i].items()
+ my_dict5[i].items() + my_dict6[i].items() + my_dict7[i].items() + my_dict8[i].items()
+ my_dict9[i].items() + my_dict10[i].items() + my_dict11[i].items() + my_dict12[i].items()
+ my_dict13[i].items() + my_dict14[i].items() + my_dict15[i].items() + my_dict16[i].items()
)
all_dict.append(temp_dict)
newX = dv.fit_transform(all_dict)
# Separate the training and testing data sets
half_cut = int(len(df)/2.0)*-1
X_train = newX[:half_cut]
X_test = newX[half_cut:]
y_train = y[:half_cut]
y_test = y[half_cut:]
# Fitting X and y into model, using training data
#$$
lr.fit(X_train, y_train)
# Making predictions using trained data
#$$
y_train_predictions = lr.predict(X_train)
#$$
y_test_predictions = lr.predict(X_test)
#print (y_train_predictions == y_train).sum().astype(float)/(y_train.shape[0])
print 'Accuracy:',(y_test_predictions == y_test).sum().astype(float)/(y_test.shape[0])
print 'Classification report:'
print classification_report(y_test, y_test_predictions)
#print sk_confusion_matrix(y_train, y_train_predictions)
print 'Confusion matrix:'
print sk_confusion_matrix(y_test, y_test_predictions)
#print y_test[1:20]
#print y_test_predictions[1:20]
#print y_test[1:10]
#print np.bincount(y_test)
#print np.bincount(y_test_predictions)
# Find and plot AUC
false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_test_predictions)
roc_auc = auc(false_positive_rate, true_positive_rate)
print 'AUC=',roc_auc
plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, true_positive_rate, 'b', label='AUC = %0.2f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
答案 0 :(得分:6)
你做错了。根据文件:
y_score : array, shape = [n_samples]
Target scores, can either be probability estimates of the positive class or confidence values.
因此在这一行:
roc_curve(y_test, y_test_predictions)
您应该将roc_curve
的{{1}}函数结果(或decision_function
结果中的两列中的一些)转换为实际预测。