CODEWITHSUNDEEP
rglm
James H
James H

Reputation: 159

R How to quickly get decision boundary for logistic regression

We know how to plot decision boundaries for logistic regression and other classifier methods, however, I am not interested in a plot; but rather I want the exact value at which the binomial prediction is .50.

For example:

train=data.frame(1:20)
train$response=rep(1:0,10)
model=glm(response ~ poly(X1.20, 2), data=train, family=binomial)


train$X1.20[1]=10.5
predict(model, train[1,], type="response")

Leaves me with a decision boundary of 10.5 which I can find through trial and error with the predict() function, meaning a value of 10.5 for the independent variable gives a response of exactly .50. Is there an automated way to find what value will give a response of .50?

Upvotes: 1

Views: 1725

Answers (1)

maccruiskeen
maccruiskeen

Reputation: 2808

You should use the fact that a predicted value of zero from the logit model implies a response probability of 0.5. So you can just try to find a value of x that makes the predicted value as close to zero as possible. Here deviationFromZero() finds how far the predicted value from the model is from zero given any value of x.

df <- data.frame(x = 1:20, response = rep(1:0, 10))
model <- glm(response ~ poly(x, 2), data = df, family = binomial)
deviationFromZero <- function(y) abs(predict(model, data.frame(x = y)))
boundary <- optimize(f = deviationFromZero, interval = range(df$x))

boundary
$minimum
[1] 10.5

$objective
           1 
1.926772e-16

Upvotes: 3

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