我是这台机器学习的新手，并使用这个波士顿数据集进行预测。 除了precision_score和accuracy_score的结果之外的所有东西都运行正常。 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：
文件“LinearRegression.py”，第33行，in
accuracy = accuracy_score(y_test, y_predict) File "/usr/local/lib/python2.7/dist-packages/sklearn/metrics/classification.py",第172行，在accuracy_score中
y_type, y_true, y_pred = _check_targets(y_true, y_pred)_check_targets中的文件“/usr/local/lib/python2.7/dist-packages/sklearn/metrics/classification.py”，第89行
raise ValueError("{0} is not supported".format(y_type)) ValueError: continuous is not supported
I'm new to this machine learning and using this boston dataset for predictions. Everything except the result for precision_score and accuracy_score is working fine . This is what i have done :
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'))Now i get the following error:
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
最满意答案
您正在使用线性回归模型
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。数字作为类别。
这就是你收到错误的原因。
怎么解决这个？
因为你试图预测波士顿数据的Price ，这是一个连续的价值。 我建议您将错误度量从准确度更改为RMSE或MSE
更换：
print(accuracy_score(y_test, y_predict))有：
from sklearn.metrics import mean_squared_error print(mean_squared_error(y_test, y_predict))这将解决您的问题。
You are using a linear Regression model as
clf = LinearRegression()which predicts continuous values. eg: 1.2, 1.3
Whereas accuracy_score(y_test, y_predict) expects boolean values. 1 or 0 (true or false) or categorical values like 1,2,3,4 etc.. Where the numbers act as categories.
That's why you are getting an error.
How to solve this?
Since you are trying to predict Price on boston data which is a continuous value. I recommend you change your error measure from accuracy to RMSE or MSE
Replace:
print(accuracy_score(y_test, y_predict))with:
from sklearn.metrics import mean_squared_error print(mean_squared_error(y_test, y_predict))That will solve your problem.
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