分类度量无法处理二进制和连续目标的混合

Question

我的输入数据文件的格式如下：

黄金，黄金，黄金，黄金

T，1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

N，0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

N，0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

N，0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

我试图根据剩余列的值预测第一列（gold），下面是我使用的代码：

import pandas as pd
import numpy as np
dataset = pd.read_csv( 'data1extended.txt', sep= ',') 
#convert T into 1 and N into 0
dataset['gold'] = dataset['gold'].astype('category').cat.codes

print(dataset.head())
row_count, column_count = dataset.shape
X = dataset.iloc[:, 1:column_count].values
y = dataset.iloc[:, 0].values

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from sklearn.ensemble import RandomForestRegressor

regressor = RandomForestRegressor(n_estimators=20, random_state=0)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))
print(accuracy_score(y_test, y_pred))

我代码的最后3行导致错误，如何修复？

此行导致错误： 打印（混淆矩阵（y_测试，y_预测）） 我打印了y_test和y_pred，以下是我获得的： y_测试为：[0 0…0 0 0]
y_pred is:[0.0007123 0.00402548 0.00402548…0.00402548 0.02651928 0.00816086]

Answer 1

您使用的是RandomForestRegressor，它输出连续值输出，即实数，而混淆矩阵期望类别值输出，即离散数输出0、1、2等等

由于您试图预测类，即1或0，您可以做两件事：

1.）使用RandomForestClassifier代替RandomForestRegressionor，后者将输出0或1，您可以使用它获取度量。（推荐）

2.）如果只需要实值输出，可以设置阈值，即

y_pred = (y_pred < threshold).astype(int)

如果输出实数小于阈值else 1，则将其转换为1，并使用它获取度量