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python - ValueError：“指标无法处理二进制和连续目标的混合”，没有来源

问题描述

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，我已确保指标彼此同步，但我当然将这个问题归咎于自己，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

标签： pythonmachine-learningscikit-learnkaggle

解决方案

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。

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