Precision_score 和 accuracy_score 显示值错误

Question

我是这个机器学习的新手，使用这个波士顿数据集进行预测。除了 precision_score 和 accuracy_score 的结果之外的所有内容都工作正常。这就是我所做的：

import pandas as pd 
import sklearn 
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing,cross_validation, svm
from sklearn.datasets import load_boston
import numpy as np
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix

boston = load_boston()
df = pd.DataFrame(boston.data)
df.columns= boston.feature_names
df['Price']= boston.target

X = np.array(df.drop(['Price'],axis=1), dtype=np.float64)
X = preprocessing.scale(X)

y = np.array(df['Price'], dtype=np.float64)

print (len(X[:,6:7]),len(y))

X_train,X_test,y_train,y_test=cross_validation.train_test_split(X,y,test_size=0.30)

clf =LinearRegression()
clf.fit(X_train,y_train)
y_predict = clf.predict(X_test)

print(y_predict,len(y_predict))
print (accuracy_score(y_test, y_predict))
print(precision_score(y_test, y_predict,average = 'macro'))

现在我收到以下错误：

File "LinearRegression.py", line 33, in
 accuracy = accuracy_score(y_test, y_predict)    File "/usr/local/lib/python2.7/dist-packages/sklearn/metrics/classification.py",
line 172, in accuracy_score
 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "/usr/local/lib/python2.7/dist-packages/sklearn/metrics/classification.py", line 89, in _check_targets
 raise ValueError("{0} is not supported".format(y_type))

 ValueError: continuous is not supported

Answer 1

您正在使用线性回归模型作为

clf = LinearRegression()

预测连续值。例如：1.2、1.3

而 accuracy_score(y_test, y_predict) 需要布尔值。 1 或 0（真或假）或分类值，如 1、2、3、4 等。其中数字作为类别。

这就是您收到错误的原因。

如何解决这个问题？

因为您正在尝试预测 Price 波士顿数据，这是一个连续值。我建议您将误差度量从准确性更改为 RMSE 或 MSE

替换：

print(accuracy_score(y_test, y_predict))

与：

from sklearn.metrics import mean_squared_error
print(mean_squared_error(y_test, y_predict))

这将解决您的问题。

Precision_score 和 accuracy_score 显示值错误

Precision_score and accuracy_score showing value error

python

machine-learning

linear-regression

scikit-learn