# Logistic Regression: How to call the output

I'm currently working on a scientific paper and I'm struggle to answer the following question: What is the right term for the output (ranging from 0 to 1) of a logistic regression?

Neither of these works (since being already taken): Certainty, probability, confidence.

Ideas: rank, probability of occurrence, probability of success..?

• "Fitted probability" or "predicted probability". Aug 3, 2016 at 7:38
• Rank is certainly wrong. That refers to first, second, third, etc., which does not correspond to the predicted probability you get from a logistic regression model. Aug 3, 2016 at 9:52

It simply is probability, you can call it "predicted" as suggested by others.

I see from the discussion that you disagree with such name, so let me proove you that this is probability.

First, recall that if $X$ is a Bernoulli distributed random variable parametrized by $p$, then $E(X) = p$. Second, take an intercept-only logistic regression model, such model will calculate mean of your predicted $Y$ variable. This would be the same as if you calculated it simply taking $\hat y_i = (1/N) \sum_{i=1}^N y_i$. This mean would converge to expected value as $N\rightarrow\infty$, i.e. to $E(Y)= p$. In fact, sample mean is a maximum likelihood estimator of $p$ for Bernoulli distributed random variable. In case of more complicated logistic regression model you predict conditional means, i.e. conditional probabilities.

If this still does not convince you, below you can see simple R example showing exactly that case:

set.seed(123)
p1 <- 0.75
Y1 <- sample(0:1, 500, replace = TRUE, prob = c(1-p1, p1))

fit1 <- glm(Y1~1, family = "binomial")

p1
## [1] 0.75

fitted(fit1)[1] # only the first one since all predictions are the same
##     1
## 0.762

mean(Y1)
## [1] 0.762

q <- 0.3
p2 <- c(0.4, 0.7)
X <- sample(0:1, 500, replace = TRUE, prob = c(1-q, q))
Y2 <- numeric(500)
Y2[X==0] <- sample(0:1, sum(X==0), replace = TRUE, prob = c(1-p2[1], p2[1]))
Y2[X==1] <- sample(0:1, sum(X==1), replace = TRUE, prob = c(1-p2[2], p2[2]))

fit2 <- glm(Y2~X, family = "binomial")

# predicted probabilities vs the true ones
table( ifelse(X==0, p2[1], p2[2]), round(fitted(fit2), 3))
##
##      0.359 0.658
##  0.4   348     0
##  0.7     0   152

# empirical conditional probabilities (conditional means)
tapply( Y2, X, mean )
##         0         1
## 0.3591954 0.6578947

• Thank you very much for the (convincing) code! The table convinced me to go for "predicted probability" ! Aug 4, 2016 at 9:32

Hand-Waving: The logistic regression is some sort of squeezing a linear regression (fitting a line through you samples) into the range [0,1]. Since you have now some numerical value in the [0,1] range you can actually cal it (TADAM!) probability!

More Rigorous: What you get is $P(y=white|X)$ or in other words the probability of some sample $y$ to be labeled as $white$ (or whatever two classes that you have) depending on $X$-the data, or more specifically the independent measures of the features represented by $y$.

So to sum it up, $logit(y)$ is the probability of $y$ being ${0,1}$. If it is only about semantics, you can always drop likelihood inside.

• I think "probability" doesn't work since the result is gained for a logistic function (sigma) which has nothing to do with the "probability" - neither the bayesian nor the probabilistic one. Same applies for the "likelihood" which is occupied by by the bayesian likelihood (resulting into a posterior distribution in combination with priori knowledge). Aug 3, 2016 at 8:52
• it's not about the logit function as itself, but as using the logit as generalized linear model to fit the data, hence it's can definitely (and usually) describes a probability (usually plotted as a CDF) Aug 3, 2016 at 9:15
• You could call it the expected proportion of successes (1s), but many just call that a probability. The fact that the relationship between that expected probablity and a continuous X is assumed to follow an S-shaped curve changes nothing. Aug 3, 2016 at 9:48

If $x$ are your independent variables then the output of the logistic regression, let's call it $l(x)$, is the predicted ''conditional'' probability , i.e. the predicted value of the probability conditional on that value of $x$.

So if you have a very large number of subjects with the same value for $x$ then it is expected that a fraction $l(x)$ of these will be 'successes' (or 1).