理解这种逻辑回归的实现

Question

按照 scikit-learn 中逻辑回归的这个示例实现： https://analyticsdataexploration.com/logistic-regression-using-python/

运行预测后，生成如下：

predictions=modelLogistic.predict(test[predictor_Vars])
predictions
array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,
       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,
       0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,
       1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
       1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,
       0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0,
       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,
       1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0,
       1, 0, 0, 0], dtype=int64)

我无法理解 array 值。我认为它们与逻辑函数有关，并且正在输出它认为标签是什么，但这些值应该在 0 和 1 之间而不是 0 或 1 之间吗？

正在阅读 predict 函数的文档：

predict(X)
Predict class labels for samples in X.
Parameters: 
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Samples.
Returns:    
C : array, shape = [n_samples]
Predicted class label per sample.

取前 5 个值：返回数组的 0、1、0、0、1 如何将它们解释为标签？

完整代码：

import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn import cross_validation


import matplotlib.pyplot as plt
%matplotlib inline

train=pd.read_csv('/train.csv')
test=pd.read_csv('/test.csv')

def data_cleaning(train):
    train["Age"] = train["Age"].fillna(train["Age"].median())
    train["Fare"] = train["Age"].fillna(train["Fare"].median())
    train["Embarked"] = train["Embarked"].fillna("S")


    train.loc[train["Sex"] == "male", "Sex"] = 0
    train.loc[train["Sex"] == "female", "Sex"] = 1

    train.loc[train["Embarked"] == "S", "Embarked"] = 0
    train.loc[train["Embarked"] == "C", "Embarked"] = 1
    train.loc[train["Embarked"] == "Q", "Embarked"] = 2

    return train

train=data_cleaning(train)
test=data_cleaning(test)

predictor_Vars = [ "Sex", "Age", "SibSp", "Parch", "Fare"]

X, y = train[predictor_Vars], train.Survived

X.iloc[:5]

y.iloc[:5]

modelLogistic = linear_model.LogisticRegression()

modelLogisticCV= cross_validation.cross_val_score(modelLogistic,X,y,cv=15)

modelLogistic = linear_model.LogisticRegression()
modelLogistic.fit(X,y)
#predict(X) Predict class labels for samples in X.
predictions=modelLogistic.predict(test[predictor_Vars])

更新：

打印测试数据集中的前 10 个元素：

可以看到它匹配数组前 10 个元素的预测：

0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0

所以这些是在将逻辑回归应用于 train 数据集后对 test 数据集的逻辑回归预测。

Answer 1

如文档中所述，predict 函数返回的值是 class 标签（就像您作为 y 提供给 fit 函数的值）。在你的情况下，1 表示存活，0 表示未存活。

如果您想要每个预测的分数，您应该使用 decision_function 其中 returns 值介于 -1 和 1 之间。

我希望这能回答你的问题。

理解这种逻辑回归的实现

Understanding this implementation of logistic regression

python

scikit-learn

logistic-regression