Hi Everyone,

In the following stan model, I have fitted a logistic regression model for training data. Then I want to calculate the misclassification error based on test data. For that I have first obtained the predicted probabilities for test data.

```
data {
int<lower=1> N1;
int<lower=1> N2;
int<lower=1> K1;
int<lower=0,upper=1> yt[N1]; //response of training data
matrix[N1,K1] x1;//training data matrix
matrix[N2,K1] x1h; // test data matrix
}
parameters {
real alpha1;
vector[K1] beta1;
}
model {
beta1 ~ normal(0, 100);
alpha1 ~ normal(0, 100);
yt ~ bernoulli_logit_glm(x1, alpha1, beta1);
}
generated quantities {
vector[N2] y_new;
y_new = inv_logit(alpha1 + x1h * beta1);//inverse logit transformation to get predictions
}
```

My questions is: Can I improve this code to improve the efficiency?

My ultimate aim is to extend this code to do K-fold cross validation.