Confusion matrix: [[ 0 0 23] [ 0 0 19] [ 0 0 18]]分析成这样是为什么

这个混淆矩阵的意思是，您的模型在一个三类分类问题中，对于真实标签为第一类、第二类和第三类的样本，预测结果都没有正确的预测出来。

具体来说，对于真实标签为第一类的样本，模型将它们全部预测成了第三类，没有一个样本预测正确；对于真实标签为第二类的样本，模型也将它们全部预测成了第三类，没有一个样本预测正确；对于真实标签为第三类的样本，模型同样将它们全部预测成了第三类，也没有一个样本预测正确。

这种情况可能是由于多种原因导致的，比如数据集分布不均，训练集过小，模型复杂度不够等等。您需要对这些问题进行分析，并尝试解决它们，以提高模型的性能。

相关问题

# 拆分数据集 X_train, X_test, y_train, y_test = train_test_split(heartbeats_image, labels, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42) # 保存数据集 np.save('X_train.npy', X_train) np.save('X_val.npy', X_val) np.save('X_test.npy', X_test) np.save('y_train.npy', y_train) np.save('y_val.npy', y_val) np.save('y_test.npy', y_test) from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout # 定义卷积神经网络 model = Sequential([ Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(255,255,1)), MaxPooling2D(pool_size=(2,2)), Conv2D(filters=64, kernel_size=(3,3), activation='relu'), MaxPooling2D(pool_size=(2,2)), Conv2D(filters=128, kernel_size=(3,3), activation='relu'), MaxPooling2D(pool_size=(2,2)), Flatten(), Dense(units=128, activation='relu'), Dropout(0.5), Dense(units=1, activation='sigmoid') ]) model.add(Dense(20, activation='softmax')) # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val)) # 保存模型 model.save('my_model.h5') from sklearn.metrics import confusion_matrix, roc_curve, auc import matplotlib.pyplot as plt # 对测试集进行预测 y_pred = model.predict(X_test) # 将预测结果转换为标签 y_pred_labels = (y_pred > 0.5).astype(int) from sklearn.metrics import confusion_matrix from sklearn.utils.multiclass import unique_labels # 将多标签指示器转换成标签数组 y_test = unique_labels(y_test) y_pred_labels = unique_labels(y_pred_labels) # 计算混淆矩阵 cm = confusion_matrix(y_test, y_pred_labels) # 绘制混淆矩阵 plt.imshow(cm, cmap=plt.cm.Blues) plt.xlabel("Predicted labels") plt.ylabel("True labels") plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], ['N','L','R','A','a','J','S','V','F','[','!',']','e','j','E','/','f','x','Q','|']) plt.yticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], ['N','L','R','A','a','J','S','V','F','[','!',']','e','j','E','/','f','x','Q','|']) plt.title('Confusion matrix') plt.colorbar() plt.show()之后怎么绘制ROC曲线

要绘制ROC曲线，需要计算模型的真正率（True Positive Rate，TPR）和假正率（False Positive Rate，FPR），可以使用sklearn.metrics库中的roc_curve函数来计算TPR和FPR，然后使用matplotlib库中的plot函数来绘制ROC曲线。

以下是绘制ROC曲线的代码示例：

from sklearn.metrics import roc_curve, auc

# 计算TPR和FPR
fpr, tpr, thresholds = roc_curve(y_test, y_pred)

# 计算AUC值
roc_auc = auc(fpr, tpr)

# 绘制ROC曲线
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()

其中，y_test是测试集的真实标签，y_pred是模型对测试集的预测结果。计算TPR和FPR时，可以使用roc_curve函数来计算，返回的fpr和tpr即为FPR和TPR，thresholds是阈值，这里用不到。计算AUC值时，可以使用auc函数来计算。绘制ROC曲线时，使用plot函数来绘制，其中color为曲线颜色，lw为线宽，label为图例标签，xlim和ylim为坐标轴范围，xlabel和ylabel为坐标轴标签，title为图表标题，legend为图例。

参考以下两段代码代码：第一段：# Lab5: Cross-Validation and the Bootstrap # The Validation Set Approach install.packages("ISLR") library(ISLR) set.seed(1) train=sample(392,196) lm.fit=lm(mpg~horsepower,data=Auto,subset=train) attach(Auto) mean((mpg-predict(lm.fit,Auto))[-train]^2) lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train) mean((mpg-predict(lm.fit2,Auto))[-train]^2) lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train) mean((mpg-predict(lm.fit3,Auto))[-train]^2) set.seed(2) train=sample(392,196) lm.fit=lm(mpg~horsepower,subset=train) mean((mpg-predict(lm.fit,Auto))[-train]^2) lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train) mean((mpg-predict(lm.fit2,Auto))[-train]^2) lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train) mean((mpg-predict(lm.fit3,Auto))[-train]^2) # Leave-One-Out Cross-Validation glm.fit=glm(mpg~horsepower,data=Auto) coef(glm.fit) lm.fit=lm(mpg~horsepower,data=Auto) coef(lm.fit) library(boot) glm.fit=glm(mpg~horsepower,data=Auto) cv.err=cv.glm(Auto,glm.fit) cv.err$delta cv.error=rep(0,5) for (i in 1:5){ glm.fit=glm(mpg~poly(horsepower,i),data=Auto) cv.error[i]=cv.glm(Auto,glm.fit)$delta[1] } cv.error第二段：library(caret) library(klaR) data(iris) splt=0.80 trainIndex <- createDataPartition(iris$Species,p=split,list=FALSE) data_train <- iris[ trainIndex,] data_test <- iris[-trainIndex,] model <- NaiveBayes(Species~.,data=data_train) x_test <- data_test[,1:4] y_test <- data_test[,5] predictions <- predict(model,x_test) confusionMatrix(predictions$class,y_test)。写出R代码完成以下任务：①建立50×30的随机数据和30个变量；②生成三组不同系数的①线性模型；③（线性回归中）分别计算这三组的CV值；④（岭回归中）分别画出这三组的两张图，两张图均以lambd为横坐标，一张图以CV error为纵坐标，一张图以Prediction error为纵坐标，两张图同分开在Plots位置，而且三组一组画完，再画另一组

以下是R代码：

①

set.seed(1)
data <- matrix(rnorm(1500), nrow = 50, ncol = 30)

②

# 第一组线性模型
lm.fit1 <- lm(data[,1] ~ data[,2]+data[,3]+data[,4]+data[,5]+data[,6]+
              data[,7]+data[,8]+data[,9]+data[,10]+data[,11]+data[,12]+
              data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+
              data[,19]+data[,20]+data[,21]+data[,22]+data[,23]+data[,24]+
              data[,25]+data[,26]+data[,27]+data[,28]+data[,29]+data[,30])

# 第二组线性模型
lm.fit2 <- lm(data[,1] ~ data[,2]+data[,3]+data[,4]+data[,5]+data[,6]+
              data[,7]+data[,8]+data[,9]+data[,10]+data[,11]+data[,12]+
              data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+
              data[,19]+data[,20]+data[,21]+data[,22]+data[,23]+data[,24]+
              data[,25]+data[,26]+data[,27]+data[,28]+data[,29]+data[,30])

# 第三组线性模型
lm.fit3 <- lm(data[,1] ~ data[,2]+data[,3]+data[,4]+data[,5]+data[,6]+
              data[,7]+data[,8]+data[,9]+data[,10]+data[,11]+data[,12]+
              data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+
              data[,19]+data[,20]+data[,21]+data[,22]+data[,23]+data[,24]+
              data[,25]+data[,26]+data[,27]+data[,28]+data[,29]+data[,30])

③

# 第一组线性模型
library(boot)
cv.fit1 <- cv.glm(data, lm.fit1)
cv.fit1$delta[1]

# 第二组线性模型
cv.fit2 <- cv.glm(data, lm.fit2)
cv.fit2$delta[1]

# 第三组线性模型
cv.fit3 <- cv.glm(data, lm.fit3)
cv.fit3$delta[1]

④

library(glmnet)

# 第一组岭回归
set.seed(1)
cv.ridge1 <- cv.glmnet(data[,2:30], data[,1], alpha = 0)
lambda.min1 <- cv.ridge1$lambda.min
ridge.fit1 <- glmnet(data[,2:30], data[,1], alpha = 0)
plot(ridge.fit1, xvar = "lambda", label = TRUE)
abline(v = lambda.min1, lty = "dashed", lwd = 1, col = "red")
ridge.pred1 <- predict(ridge.fit1, newx = data[,2:30], s = lambda.min1)
ridge.cv.error1 <- mean((ridge.pred1 - data[,1])^2)
ridge.cv.error1

# 第二组岭回归
set.seed(1)
cv.ridge2 <- cv.glmnet(data[,2:30], data[,1], alpha = 0.5)
lambda.min2 <- cv.ridge2$lambda.min
ridge.fit2 <- glmnet(data[,2:30], data[,1], alpha = 0.5)
plot(ridge.fit2, xvar = "lambda", label = TRUE)
abline(v = lambda.min2, lty = "dashed", lwd = 1, col = "red")
ridge.pred2 <- predict(ridge.fit2, newx = data[,2:30], s = lambda.min2)
ridge.cv.error2 <- mean((ridge.pred2 - data[,1])^2)
ridge.cv.error2

# 第三组岭回归
set.seed(1)
cv.ridge3 <- cv.glmnet(data[,2:30], data[,1], alpha = 1)
lambda.min3 <- cv.ridge3$lambda.min
ridge.fit3 <- glmnet(data[,2:30], data[,1], alpha = 1)
plot(ridge.fit3, xvar = "lambda", label = TRUE)
abline(v = lambda.min3, lty = "dashed", lwd = 1, col = "red")
ridge.pred3 <- predict(ridge.fit3, newx = data[,2:30], s = lambda.min3)
ridge.cv.error3 <- mean((ridge.pred3 - data[,1])^2)
ridge.cv.error3