CODEWITHSUNDEEP
pythonscikit-learnlogistic-regression
blue-sky
blue-sky

Reputation: 53806

Understanding this implementation of logistic regression

Following this example implementation of Logistic Regression from scikit-learn : https://analyticsdataexploration.com/logistic-regression-using-python/

After running predict , the following is produced :

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)

I'm failing to understand the array values. I think they are related to logistic function and are outputting what it thinks the label is but should these values be between 0 and 1 instead of 0 or 1 ?

Reading the doc for predict function : 

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.

Taking the first 5 values : 0, 1, 0, 0, 1 of the returned array how are these interpreted as labels ?

Complete code :

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])

Update :

printing first 10 elements from the test dataset :

enter image description here

Can see it matches the predictions of first 10 elements of array : 

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

So these are the logistic regression predictions on the test dataset after applying logistic regression to the train dataset.

Upvotes: 0

Views: 182

Answers (1)

Darkoob12
Darkoob12

Reputation: 114

As stated in the documentation the values returned by the predict function are class labels (like the values you provided to the fit function as y). In your case 1 for survived and 0 for not survived.

If you want the scores of each prediction you should use the decision_function which returns values between -1 and 1.

i hope this answers your question.

Upvotes: 2

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