展开混淆矩阵插入信息

Question

在我想要的每个表面上，预测的实际数量。我真的不在乎它只是百分比还是数字。我还想用真阳性和假阴性标记它们。

代码：

sns.heatmap(pd.crosstab(ytest,classifier.predict(xtest)),cmap='Spectral')

plt.xlabel('predicted')

plt.ylabel('actual')

plt.show()

Answer 1

我使用下面的内容来做你想做的事，不过 google 搜索也会给你答案

def find_best_threshold(threshold, fpr, tpr):
    t = threshold[np.argmax(tpr * (1-fpr))]
    ### TPR * TNR ---> We are trying to maximize TNR and TPR
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    return t

def predict_with_best_thresh(prob,t):
    pred=[1 if i>=t else 0 for i in prob  ]
    return pred

### https://medium.com/@dtuk81/confusion-matrix-visualization-fc31e3f30fea
def conf_matrix_plot(cf_matrix,title):
    group_names = ['True Neg','False Pos','False Neg','True Pos']
    group_counts = ["{0:0.0f}".format(value) for value in cf_matrix.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in cf_matrix.flatten()/np.sum(cf_matrix)]
    labels = [f"{v1}\n{v2}\n{v3}" for v1, v2, vQ3 in zip(group_names,group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    #sns.set(font_scale=1.5) 
    sns.heatmap(cf_matrix, annot=labels, fmt='',cmap='coolwarm').set_title(title + ' Confusion Matrix for TFIDF')
    plt.xlabel('Actual')
    plt.ylabel('Predicted')

from sklearn.metrics import confusion_matrix
import numpy as np
best_t = find_best_threshold(tr_thresholds, train_fpr, train_tpr)
cf_matrix_train = confusion_matrix(y_train, predict_with_best_thresh(y_train_pred[:,1], best_t))
cf_matrix_test = confusion_matrix(y_test, predict_with_best_thresh(y_test_pred[:,1], best_t))

conf_matrix_plot(cf_matrix_train,'Train')

结果：

展开混淆矩阵插入信息

Expand Confusion Matrix Insert Information

python

classification

machine-learning

heatmap

confusion-matrix