python confusion_matrix()是什么 说明 1、计算分类器预测结果的混淆矩阵C。 2、混淆矩阵C使得C_ij等于已知在第i组中并且预计在第j组中的观测次数。...cm = confusion_matrix(Y_test, Y_predict) print(cm) 以上就是python confusion_matrix()的介绍，希望对大家有所帮助。
confusion_matrix()用法如下： from sklearn.metrics import confusion_matrix y_true = ["cat", "ant", "cat", "...cat", "ant", "bird"] y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] confusion_matrix(y_true, y_pred
y_pred: 是样本预测分类结果 labels：是所给出的类别，通过这个可对类别进行选择 sample_weight : 样本权重 实现例子： from sklearn.metrics import confusion_matrix...y_true=[2,1,0,1,2,0] y_pred=[2,0,0,1,2,1] C=confusion_matrix(y_true, y_pred) 运行结果： [[1 1 0] [1 1 0]
2.编程实现混淆矩阵 使用sklearn.metrics模块中的confusion_matrix()函数对混淆矩阵中的数据进行观察。...confusion_matrix()函数的使用方法如下 from sklearn.metrics import confusion_matrix confmat = confusion_matrix(y_true
matlab代码 下面是我写的matlab代码仅供参考 confusion_matrix=[239 21 16; 16 73 4;...6 9 280]; [row col]=size(confusion_matrix);%获取矩阵的行和列 fenleizhengque_yangben=diag(confusion_matrix);...%分类正确的样本就是对角线上的值，这是一个列向量 yangbenzongshu=sum(confusion_matrix(:)); p0=sum(fenleizhengque_yangben)/yangbenzongshu...就用百度词条里的来算 a=sum(confusion_matrix,1);%第2个参数为1是按列求值，把同一列的数加起来,这是行向量 b=sum(confusion_matrix,2);%第2个参数为2...% a=sum(confusion_matrix,2);%第2个参数为2是按行求值，把同一行的数加起来,这是列向量 % b=sum(confusion_matrix,1);%第2个参数为1是按列求值，把同一列的数加起来
import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix...20) rf_model.fit(X_train, y_train) rf_pred = rf_model.predict(X_test) print("RF all feature") print(confusion_matrix...hsic_x_train, y_train) rf_hsic_pred = rf_hsic_model.predict(hsic_x_test) print("RF HSIC feature") print(confusion_matrix...import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix...20) rf_model.fit(X_train, y_train) rf_pred = rf_model.predict(X_test) print("RF all feature") print(confusion_matrix
import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import confusion_matrix...dtc.score(X_test, y_test) st.write('Accuracy: ', acc) pred_dtc = dtc.predict(X_test) cm_dtc=confusion_matrix...svm.score(X_test, y_test) st.write('Accuracy: ', acc) pred_svm = svm.predict(X_test) cm=confusion_matrix...import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import confusion_matrix...svm.score(X_test, y_test) st.write('Accuracy: ', acc) pred_svm = svm.predict(X_test) cm=confusion_matrix
# 输出混淆矩阵 from sklearn.metrics import classification_report,confusion_matrix confusion_matrix = confusion_matrix...(y_test, y_pred_rf) print(confusion_matrix) # 绘制混淆矩阵热力图 # 创建总画布窗口 plt.figure(figsize=(8,6)) # 绘制热力图...y_train) y_pred_xgbt = xgbt.predict(x_test) # 输出混淆矩阵 from sklearn.metrics import classification_report,confusion_matrix...confusion_matrix = confusion_matrix(y_test, y_pred_xgbt) print(confusion_matrix) # 绘制混淆矩阵热力图 # 创建总画布窗口...热力图的每个单元上显示数值；annot_kws:设置单元格中数值标签的其他属性； # fmt：指定单元格中数据的显示格式；cmap：于热力图的填充色，'YlGnBu_r'代表数字越大，颜色越浅 sns.heatmap(confusion_matrix
分类 - 混淆矩阵 Confusion Matrix sklearn.metrics.confusion_matrix from sklearn.metrics import confusion_matrix...image.png C = confusion_matrix(gt_labels, pred_labels, labels=None, sample_weight=None)[source] # C...gt_labels = [2, 0, 2, 2, 0, 1] pred_labels = [0, 0, 2, 2, 0, 2] confusion_matrix(gt_labels, pred_labels...) # array([[2, 0, 0], # [0, 0, 1], # [1, 0, 2]]) 示例2： from sklearn.metrics import confusion_matrix...tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() #(tn, fp, fn, tp) #(0, 2, 1, 1
I miss you,真实为：[0] 2.1 性能指标 混淆矩阵 from sklearn.metrics import confusion_matrix import matplotlib.pyplot...as plt confusion_matrix = confusion_matrix(y_test, pred) plt.matshow(confusion_matrix) plt.rcParams[...sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, accuracy_score, confusion_matrix...X_test) print('Accuracy: %s' % accuracy_score(y_test, predictions)) print('Confusion Matrix:') print(confusion_matrix
from sklearn.metrics import confusion_matrix import numpy as np def compute_iou(y_pred, y_true):...ypred is a flatten vector y_pred = y_pred.flatten() y_true = y_true.flatten() current = confusion_matrix
from matplotlib import pyplot as plt import numpy as np classes = ['类1', '类2', '类3', '类4'] confusion_matrix...100, 1000, 100), (300, 200, 100, 1200)]) proportion = [] # 百分比 for i in confusion_matrix...for j in range(len(classes)): if i == j: # 背景色太深了，设字体为白色 plt.text(j, i - 0.1, s=confusion_matrix...va='center', ha='center', color='white') # 显示百分比 else: plt.text(j, i - 0.1, s=confusion_matrix
import roc_curve, auc from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix...rf.predict(X_test) y_pred_quant = rf.predict_proba(X_test)[:,1] y_pred_bin = rf.predict(X_test) # 混淆矩阵 confusion_matrix...= confusion_matrix(y_test,y_pred_bin) confusion_matrix # 计算sensitivity and specificity total=sum(sum...(confusion_matrix)) sensitivity = confusion_matrix[0,0]/(confusion_matrix[0,0]+confusion_matrix[1,0])...specificity = confusion_matrix[1,1]/(confusion_matrix[1,1]+confusion_matrix[0,1]) [008i3skNgy1gyw1m75fwtj31c80r4wiz.jpg
本篇文章我们再来学习另外一个评估方法，即混淆矩阵（confusion_matrix）。...这里我们用代码演示三分类问题混淆矩阵（这里我们用confusion_matrix生成矩阵数据，然后用seaborn的热度图绘制出混淆矩阵数据），如下： #导入依赖包 import seaborn as...cat", "dog", "cat", "cat", "dog", "rebit"] y_pred = ["dog", "dog", "rebit", "cat", "dog", "cat"] C2= confusion_matrix
= table(test_predict,test_target) dimnames(confusion_matrix) confusion_matrix)) } #执行分类任务 result <- datingClassTest( test_data = test_data, train_data...train_target = train_target, test_target = test_target ) 预测结果收集与混洗矩阵输出： result$test_data result$confusion_matrix...print(classification_report(test_target,test_predict, target_names=target_names)) return test_data,confusion_matrix...t0 = time.time() train_data,test_data,train_target,test_target = Data_Input() test_reslut,confusion_matrix
模型预测使用训练好的模型对测试集进行预测：# 进行预测y_pred = knn.predict(X_test)模型评估我们将使用混淆矩阵和准确率来评估模型的性能：from sklearn.metrics import confusion_matrix..., accuracy_score# 计算混淆矩阵conf_matrix = confusion_matrix(y_test, y_pred)print("混淆矩阵:\n", conf_matrix)#...grid_search.best_estimator_best_knn.fit(X_train, y_train)# 进行预测y_best_pred = best_knn.predict(X_test)# 计算混淆矩阵和准确率best_conf_matrix = confusion_matrix...)rf_model.fit(X_train, y_train)# 进行预测rf_pred = rf_model.predict(X_test)# 计算混淆矩阵和准确率rf_conf_matrix = confusion_matrix...svm_model.fit(X_train, y_train)# 进行预测svm_pred = svm_model.predict(X_test)# 计算混淆矩阵和准确率svm_conf_matrix = confusion_matrix
# coding=UTF-8 from sklearn import metrics from sklearn.metrics import confusion_matrix from sklearn.metrics...#模型预测值 Problist = [1,0,1,1,1,1,1,1,0,1] y_true = np.array(GTlist) y_pred = np.array(Problist) #混淆矩阵 confusion_matrix...= confusion_matrix(y_true, y_pred) print("混淆矩阵:") print(confusion_matrix) #准确性 accuracy = '{:.1%}
训练集上拟合逻辑回归 classifier = LogisticRegression(random_state=0) classifier.fit(X_train, y_train) 预测测试集结果并创建混淆矩阵 confusion_matrix...y_pred = classifier.predict(X_test) from sklearn.metrics import confusion_matrix confusion_matrix = confusion_matrix...(y_test, y_pred) print(confusion_matrix) 结果告诉我们，我们有9046 + 229个正确的预测和912 + 110个不正确的预测。
当数据点的实际类和预测类都为0 -假阳（FP）− 当数据点的实际类别为0，预测的数据点类别为1 -假阴（FN）− 当数据点的实际类别为1，预测的数据点类别为0 我们可以使用sklearn的混淆矩阵函数confusion_matrix...from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.metrics...log_loss X_actual = [1, 1, 0, 1, 0, 0, 1, 0, 0, 0] Y_predic = [1, 0, 1, 1, 1, 0, 1, 1, 0, 0] results = confusion_matrix
Python混淆矩阵的使用 confusion_matrix函数的使用 官方文档中给出的用法是 sklearn.metrics.confusion_matrix(y_true, y_pred, labels...是样本预测分类结果 labels：是所给出的类别，通过这个可对类别进行选择 sample_weight : 样本权重 实现代码： Python from sklearn.metrics import confusion_matrix...y_true = [2, 1, 0, 1, 2, 0] y_pred = [2, 0, 0, 1, 2, 1] C=confusion_matrix(y_true, y_pred) print(C..."cat", "ant", "cat", "cat", "ant", "bird"] y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] C2 = confusion_matrix
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