Hello StackOverflow community,

I’m working with a dataset comprising binary vectors. For each instance in my dataset, there is an input vector X and an output vector Y. The structure of these vectors is as follows:

X is a binary vector of length n (e.g., X(1,:) = [0/1, 0/1, ..., 0/1]).
Y is a binary vector of length m, where m < n (e.g., Y(1,:) = [0/1, 0/1, ..., 0/1]).

For each X => Y
the data is like that : for example a sample :

X = [1,0,1,1,1,0,1,0,1,0,1,1,0,1] and its Y=[0,1,1,1,1,0,1]

My objective is to develop a machine learning model M that can predict the vector Y from the vector X with an accuracy greater than 95%.

I mean Machinelearning(X) => Y  it predict the Y 

Given the nature of my data (binary vectors, with n > m), I’m seeking advice on the most suitable model to achieve this goal.