ValueError: "metrics can't handle a mix of binary and continuous targets" with no source
ValueError: "metrics can't handle a mix of binary and continuous targets" with no source
我是机器学习的初学者，我正在尝试通过解决 Kaggle 的泰坦尼克号问题来学习。据我所知，我已经确保指标彼此同步，但当然我要为这个问题责怪自己，而不是 Python。但是，我仍然找不到源代码，Spyder IDE 也无济于事。
这是我的代码：
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
"""Assigning the train & test datasets' adresses to variables"""
train_path = "C:\Users\Omar\Downloads\Titanic Data\train.csv"
test_path = "C:\Users\Omar\Downloads\Titanic Data\test.csv"
"""Using pandas' read_csv() function to read the datasets
and then assigning them to their own variables"""
train_data = pd.read_csv(train_path)
test_data = pd.read_csv(test_path)
"""Using pandas' factorize() function to represent genders (male/female)
with binary values (0/1)"""
train_data['Sex'] = pd.factorize(train_data.Sex)[0]
test_data['Sex'] = pd.factorize(test_data.Sex)[0]
"""Replacing missing values in the training and test dataset with 0"""
train_data.fillna(0.0, inplace = True)
test_data.fillna(0.0, inplace = True)
"""Selecting features for training"""
columns_of_interest = ['Pclass', 'Sex', 'Age']
"""Dropping missing/NaN values from the training dataset"""
filtered_titanic_data = train_data.dropna(axis=0)
"""Using the predictory features in the data to handle the x axis"""
x = filtered_titanic_data[columns_of_interest]
"""The survival (what we're trying to find) is the y axis"""
y = filtered_titanic_data.Survived
"""Splitting the train data with test"""
train_x, val_x, train_y, val_y = train_test_split(x, y, random_state=0)
"""Assigning the DecisionTreeRegressor model to a variable"""
titanic_model = DecisionTreeRegressor()
"""Fitting the x and y values with the model"""
titanic_model.fit(train_x, train_y)
"""Predicting the x-axis"""
val_predictions = titanic_model.predict(val_x)
"""Assigning the feature columns from the test to a variable"""
test_x = test_data[columns_of_interest]
"""Predicting the test by feeding its x axis into the model"""
test_predictions = titanic_model.predict(test_x)
"""Printing the prediction"""
print(val_predictions)
"""Checking for the accuracy"""
print(accuracy_score(val_y, val_predictions))
"""Printing the test prediction"""
print(test_predictions)
这是堆栈跟踪：
Traceback (most recent call last):
File "<ipython-input-3-73797c87986e>", line 1, in <module>
runfile('C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py', wdir='C:/Users/Omar/Downloads/Kaggle Competition')
File "C:\Users\Omar\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)
File "C:\Users\Omar\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
print(accuracy_score(val_y, val_predictions))
File "C:\Users\Omar\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 176, in accuracy_score
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\Omar\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 81, in _check_targets
"and {1} targets".format(type_true, type_pred))
ValueError: Classification metrics can't handle a mix of binary and continuous targets
您正在使用 DecisionTreeRegressor，正如它所说，它是一个回归模型。 Kaggle Titanic 问题是一个分类问题。所以你应该使用 DecisionTreeClassifier。
至于为什么您的代码会抛出错误，这是因为 val_y 具有二进制值 (0,1) 而 val_predictions 具有连续值，因为您使用了回归模型。
您正在尝试对二元分类问题使用回归算法 (DecisionTreeRegressor)；正如预期的那样，回归模型给出了连续的输出，但是 accuracy_score，错误实际发生的地方：
File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
print(accuracy_score(val_y, val_predictions))
需要二进制，因此会出错。
对于初学者，请将模型更改为
from sklearn.tree import DecisionTreeClassifier
titanic_model = DecisionTreeClassifier()
分类需要离散标签，因为它预测 class（这是标签中的任何一个），回归适用于连续数据。由于您的输出是 class 标签，您需要执行 classification
我是机器学习的初学者，我正在尝试通过解决 Kaggle 的泰坦尼克号问题来学习。据我所知，我已经确保指标彼此同步，但当然我要为这个问题责怪自己，而不是 Python。但是，我仍然找不到源代码，Spyder IDE 也无济于事。
这是我的代码：
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
"""Assigning the train & test datasets' adresses to variables"""
train_path = "C:\Users\Omar\Downloads\Titanic Data\train.csv"
test_path = "C:\Users\Omar\Downloads\Titanic Data\test.csv"
"""Using pandas' read_csv() function to read the datasets
and then assigning them to their own variables"""
train_data = pd.read_csv(train_path)
test_data = pd.read_csv(test_path)
"""Using pandas' factorize() function to represent genders (male/female)
with binary values (0/1)"""
train_data['Sex'] = pd.factorize(train_data.Sex)[0]
test_data['Sex'] = pd.factorize(test_data.Sex)[0]
"""Replacing missing values in the training and test dataset with 0"""
train_data.fillna(0.0, inplace = True)
test_data.fillna(0.0, inplace = True)
"""Selecting features for training"""
columns_of_interest = ['Pclass', 'Sex', 'Age']
"""Dropping missing/NaN values from the training dataset"""
filtered_titanic_data = train_data.dropna(axis=0)
"""Using the predictory features in the data to handle the x axis"""
x = filtered_titanic_data[columns_of_interest]
"""The survival (what we're trying to find) is the y axis"""
y = filtered_titanic_data.Survived
"""Splitting the train data with test"""
train_x, val_x, train_y, val_y = train_test_split(x, y, random_state=0)
"""Assigning the DecisionTreeRegressor model to a variable"""
titanic_model = DecisionTreeRegressor()
"""Fitting the x and y values with the model"""
titanic_model.fit(train_x, train_y)
"""Predicting the x-axis"""
val_predictions = titanic_model.predict(val_x)
"""Assigning the feature columns from the test to a variable"""
test_x = test_data[columns_of_interest]
"""Predicting the test by feeding its x axis into the model"""
test_predictions = titanic_model.predict(test_x)
"""Printing the prediction"""
print(val_predictions)
"""Checking for the accuracy"""
print(accuracy_score(val_y, val_predictions))
"""Printing the test prediction"""
print(test_predictions)
这是堆栈跟踪：
Traceback (most recent call last):
File "<ipython-input-3-73797c87986e>", line 1, in <module>
runfile('C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py', wdir='C:/Users/Omar/Downloads/Kaggle Competition')
File "C:\Users\Omar\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)
File "C:\Users\Omar\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
print(accuracy_score(val_y, val_predictions))
File "C:\Users\Omar\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 176, in accuracy_score
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\Omar\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 81, in _check_targets
"and {1} targets".format(type_true, type_pred))
ValueError: Classification metrics can't handle a mix of binary and continuous targets
您正在使用 DecisionTreeRegressor，正如它所说，它是一个回归模型。 Kaggle Titanic 问题是一个分类问题。所以你应该使用 DecisionTreeClassifier。
至于为什么您的代码会抛出错误，这是因为 val_y 具有二进制值 (0,1) 而 val_predictions 具有连续值，因为您使用了回归模型。
您正在尝试对二元分类问题使用回归算法 (DecisionTreeRegressor)；正如预期的那样，回归模型给出了连续的输出，但是 accuracy_score，错误实际发生的地方：
File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
print(accuracy_score(val_y, val_predictions))
需要二进制，因此会出错。
对于初学者，请将模型更改为
from sklearn.tree import DecisionTreeClassifier
titanic_model = DecisionTreeClassifier()
分类需要离散标签，因为它预测 class（这是标签中的任何一个），回归适用于连续数据。由于您的输出是 class 标签，您需要执行 classification