How to interpret the UCLA "adjusted count" logistic regression pseudo $R^2?$ Here, UCLA gives a number of pseudo $R^2$ values for evaluating logistic regression models. Despite the issues with doing this, the last two deal with hard classifications rather than the probabilistic model outputs.
The second-to-last pseudo $R^2$, "count", makes sense, as it is just the proportion classified correctly ("accuracy"). What is the interpretation of the final pseudo $R^2$, the "adjusted count"?
$$
R^2_{\text{AdjustedCount}} = \dfrac{\text{
Correct - n
}}{\text{
Total - n
}}
$$

This equals the decrease in error rate that I discuss here and call $R^2_{accuracy}$, though it takes some algebra to see why.
$$
R^2_{\text{accuracy}} = 1 - \dfrac{
\text{Error rate of the model under consideration}
}{
\text{Error rate of a model that naïvely predicts the majority class every time}
}
$$
To simplify the calculation, I will shorten the notation.
$$
E_1 = \text{Error rate of the model under consideration} $$$$
E_0 = \text{Error rate of a model that naïvely predicts the majority class every time} $$$$
N = \text{Number of classification attempts (Sample size)}
$$
$$
R^2_{\text{accuracy}} = 1 - \dfrac{
E_1
}{
E_0
}=  \dfrac{
E_0 - E_1
}{
E_0
}
$$
Next, let's break down what the three components of the UCLA fraction mean in this terminology.
For "correct", multiply the accuracy of your model by the total number of classification attempts. Since $E_1$ is the error rate of your model, $1-E_1$ is the accuracy, so $\text{Correct} = N(1-E_1)$.
For "n", apply similar logic but to the model that naïvely predicts the majority class every time. The error rate for such a model is $E_0$, so its accuracy is $1-E_0$. Consequently, the total number of correct predictions by the model that naïvely predicts the majority class every time is $N(1-E_0)$.
Finally, "total" is easy: it's exactly $N$.
Now it's time to plug in and do the algebra.
$$
R^2_{\text{AdjustedCount}} = \dfrac{\text{
Correct - n
}}{\text{
Total - n
}}
= \dfrac{
N(1-E_1) - N(1-E_0)
}{
N - N(1-E_0)
}$$$$
= \dfrac{
(1-E_1) - (1-E_0)
}{
1 - (1-E_0)
}$$$$
= \dfrac{
1-E_1 - (1-E_0)
}{
1 - (1-E_0)
}$$$$
=\dfrac{
1-E_1 - 1 + E_0
}{
1-1+E_0
}$$$$
=\dfrac{
E_0 - E_1
}{
E_0
} $$$$
=\dfrac{E_0}{E_0}-\dfrac{E_1}{E_0}
$$$$
= 1 -\dfrac{
E_1
}{
E_0
}$$$$
= R^2_{\text{accuracy}}
$$
$\square$
EDIT
An R simulation could be fun to show the two to be equal.
set.seed(2023)
R <- 10000 # Number of times to repeat the loop
N <- 1000  # Number of samples within each loop

# Function to calculate UCLA's "count"
#
count <- function(correct, total_count){t
  return(
    correct/total_count
  )
}

# Function to calculate UCLA's "adjusted count"
#
count_adj <- function(correct, total_count, n_most_common){ 
  return(
    (correct - n_most_common)
    /
      (total_count - n_most_common)
  )
}

# Function to calculate my R^2_accuracy
#
r2_accuracy <- function(model_error_rate, naive_error_rate){
  return(
    1 -
      (model_error_rate)/(naive_error_rate)
  )
}

# Blank vector to hold differences between adjusted count and R^2_accuracy
#
d <- rep(NA, R)

# Loop R-many times
#
for (i in 1:R){
  
  # Define the true event probabilities
  #
  p1 <- runif(N, 0.1, 0.9)
  
  # Simulate 0/1 events with probability p1
  #
  true <- rbinom(N, 1, p1)
  
  # Define probability of a model making a mistake
  #
  p2 <- runif(N, 0.1, 0.9)
  
  # Define the predictions as the true values plus some noise term
  # Then mod by 2 so all values are 0 or 1
  #
  pred <- (true + rbinom(N, 1, p2)) %% 2
  
  # Define the number of correct predictions
  #
  n_correct <- length(true) - sum((true - pred)^2)
  
  # Define the sample size
  #
  total_count <- length(true)
  
  # Define the number of values belonging to the most common label
  #
  n_most_common <- max(table(true))
  
  # Define the accuracy of the predictions using the "count" function
  # (Yes, it's proportion classified correctly instead of accuracy percentage)
  #
  model_accuracy <- count(length(true) - sum((true - pred)^2), length(true))
  
  # Define the error rate of the predictions
  #
  model_error_rate <- 1 - model_accuracy
  
  # Define the accuracy of naively predicting the majority category every time
  # (Yes, it's proportion classified correctly instead of accuracy percentage)
  #
  naive_accuracy <- max(table(true))/length(true)
  
  # Define the error rate of naively predicting the majority category every time
  #
  naive_error_rate <- 1 - naive_accuracy
  
  # Calculate and store the difference between UCLA's adjusted count and
  # my R^2_ accuracy
  #
  d[i] <-
    count_adj(
      n_correct, 
      total_count, 
      n_most_common 
    ) - 
    r2_accuracy(
      model_error_rate, 
      naive_error_rate 
    )
}

# Print a summary of the differences between my calculation and UCLA's,
# revealing the two to be the same (up to differences that can be attributed 
# to doing math on a computer (floating point errors))
#
summary(d)

################################################################################
#
# OUTPUT
#
################################################################################

> summary(d)
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-2.498e-16 -2.776e-17  1.344e-17  2.025e-17  6.939e-17  2.776e-16 

The differences between my calculations and the UCLA adjusted count calculations are on the order of $10^{-16}$. This is R's way of saying the difference between the UCLA adjusted count and my $R^2_{accuracy}$ is zero every time out of ten-thousand checks. (Such differences are attributable to floating point errors coming from doing math on a computer.)
