Prediction of a binary variable

I am establishing a model for prediction of a binary variable (Yes/No) depending on three continuous variables (A,B,C). I applied logistic regression analysis for a learning dataset vith the Tanagra software, and the results were good with high prediction accuracy.

My question is: is it possible to get the probabilities of the prediction via logistic regression? Something like (0.7 Yes)? If not, what test do I have to use to get such a result?


A binary logistic regression is generally used for fitting a model to a binary output, but formally the results of logistic regression are not themselves binary, they are continuous probability values (pushed to zero or 1 by a logit transformaion, but continuous between 0 and 1 nonetheless). It sounds like the software you are using is rounding the output for you, which you don’t want. Here’s a simple example demonstrating how you could accomplish this in R, since it sounds like you are amenable to trying new software:

# generate sample data
x = rnorm(100) 
y= as.numeric(x>0)

# let's shuffle a handful so we don't fit a perfect model
ix = sample(1:100, 10)
y[ix]= 1-y[ix]

# Let's take a look at our observations
df = data.frame(x,y)

enter image description here

# Build the model
m = glm(y~x, family=binomial(logit), data=df)

# Look at results

# generate predictions. Here, since I'm not passing in new data
# it will use the training data set to generate predictions
y.pred = predict(m, type="response")
plot(x, y.pred, col=(round(y.pred)+1))

enter image description here

Source : Link , Question Author : Error404 , Answer Author : David Marx

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