逻辑回归的系数写入 python 中的函数

Question

我刚刚完成逻辑回归。数据可以从下面link下载： pleas click this link to download the data

下面是逻辑回归的代码。

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score
import pandas as pd
scaler = StandardScaler()

data = pd.read_csv('data.csv')
dataX = data.drop('outcome',axis =1).values.astype(float)
X     = scaler.fit_transform(dataX)
dataY = data[['outcome']]
Y = dataY.values

X_train,X_test,y_train,y_test = train_test_split (X,Y,test_size = 0.25, random_state = 33)

lr = LogisticRegression()
lr.fit(X_train,y_train)

# Predict the probability of the testing samples to belong to 0 or 1 class
predicted_probs = lr.predict_proba(X_test)
print(predicted_probs[0:3])
print(lr.coef_)

我可以打印逻辑回归系数，我可以计算事件发生 1 或 0 的概率。

当我使用这些系数编写 python 函数并计算发生概率 1 时。与使用此函数相比，我没有得到答案：lr.predict_proba(X_test)

我写的函数如下：

def xG(bodyPart,shotQuality,defPressure,numDefPlayers,numAttPlayers,shotdist,angle,chanceRating,type):
coeff = [0.09786083,2.30523761, -0.05875112,0.07905136, 
         -0.1663424 ,-0.73930942,-0.10385882,0.98845481,0.13175622]

return  (coeff[0]*bodyPart+ coeff[1]*shotQuality+coeff[2]*defPressure+coeff[3]*numDefPlayers+coeff[4]*numAttPlayers+coeff[5]*shotdist+ coeff[6]*angle+coeff[7]*chanceRating+coeff[8]*type)

我得到了奇怪的答案。我知道函数计算错了。

我是机器学习和统计学的新手，请问您的建议。

Answer 1

我认为您错过了 xG 中的 intercept_。您可以从 lr.intercept_ 中检索它，它应该在最终公式中求和：

return 1/(1+e**(-(intercept + coeff[0]*bodyPart+ coeff[1]*shotQuality+coeff[2]*defPressure+coeff[3]*numDefPlayers+coeff[4]*numAttPlayers+coeff[5]*shotdist+ coeff[6]*angle+coeff[7]*chanceRating+coeff[8]*type))

逻辑回归的系数写入 python 中的函数

coefficient from logistic regression to write function in python

python

machine-learning

scikit-learn

logistic-regression

lasso-regression