ValueError：“指標無法處理二進制目標和連續目標的混合”，沒有來源

Question

我是機器學習的初學者，並且嘗試通過解決Kaggle的《泰坦尼克號》問題來學習。 據我所知，我已經確保指標相互同步，但是我當然要為這個問題而不是Python負責。 但是，我仍然找不到源，Spyder IDE也無濟於事。

這是我的代碼：

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)

這是堆棧跟蹤：

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

Answer 1

您正在使用DecisionTreeRegressor，正如它所說的，它是一個回歸模型。 Kaggle Titanic問題是分類問題。 因此，您應該使用DecisionTreeClassifier。

至於為什么代碼會引發錯誤，這是因為val_y具有二進制值(0,1)而val_predictions具有連續值是因為您使用了回歸模型。

Answer 2

您正在嘗試將回歸算法（ DecisionTreeRegressor ）用於二進制分類問題； 回歸模型，符合市場預期，給出了連續輸出，但accuracy_score ，在錯誤實際發生：

File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
    print(accuracy_score(val_y, val_predictions)) 

期望二進制的，因此錯誤。

對於初學者，請將模型更改為

from sklearn.tree import DecisionTreeClassifier

titanic_model = DecisionTreeClassifier()

Answer 3

分類需要離散的標簽，因為它可以預測類（標簽中的任何一個），並且回歸適用於連續數據。 由於您的輸出是類標簽，因此需要執行分類