Logistic Regression - Machine Learning

Question

Logistic Regression with inputs of "Machine Learning.csv" file.

#Import Libraries

import pandas as pd

#Import Dataset
dataset = pd.read_csv('Machine Learning Data Set.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 10]

#Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 0)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

#Fitting Logistic Regression to the Training Set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train,y_train)

#Predicting the Test set results
y_pred = classifier.predict(X_test)

#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)

I have a machine learning / logistic regression code (python) as above. It has properly trained my model and gives a really good match with the test data. But unfortunately it is only giving me 0/1 (binary) results when I test with some other random values. (the training set has only 0/1 - as in failed/succeeded)

How can I get a probability result instead of a binary result in this algorithm? I have tried very different set of numbers and would like find out a probability of failing - instead of a 0 and 1.

Any help is strongly appreciated :) Thanks a lot!

Answer 1

Just replace

y_pred = classifier.predict(X_test)

with

y_pred = classifier.predict_proba(X_test)

For details refer Logistic Regression Probability

Answer 2

predict_proba(X_test) will give you probability of each sample for each class.ie if X_test contains n_samples and you have 2 classes output of above function will be a "n_samples X 2 " matrix. and sum of two classes predicted will be 1. for more details have a look at documentation here