# standardize dataset with numerical and dummy features

I have a dataset with both numerical and categorical features (variables), I converted all the categorical variables in dummies, then I split the train and test data.

Now I am at the step where I want to standardize the features before fitting the model.

1. Should I apply the standardisation to all the features or only to the numerical ones?

2. In this case, is it preferable to use the MinMax scaler on a range 0 to 1?

$$z_i=\frac{x_i-\min(x)}{\max(x)-\min(x)}$$