I've been trying to teach myself machine learning using some books, kaggle and other resources so I'm still very new at this. I'm trying to do a competition on kaggle and I've obtained a dataset.
I'm a little confused about what exactly needs to be done with this data set next though. My assumption is that if I choose to use a univariate distribution to model this data, I need to pick the features(columns in my csv file) I want to use, somehow combine them into a single variable xi and pass each pair of values of and labels (xi,yi) into my model, adjusting the model's parameters to fit the data.
I'm assuming that if I chose a bivariate distribution to model this data, I would do something similar, except I would convert the features of my choice into two separate values x1,x2 and pass tuples (xi1,xi2,yi) into my model and adjust parameters to fit the data.
for an n-variate distribution I would do... the same thing and pass in a vector (xi1, ..., xin, yi) into my model.
Is this pattern of thinking correct? I know I'm not mentioning anything about model or feature selection but that's mainly because I feel like I need to understand this step between training data --> model before I can even try to implement a likelihood estimation algorithm or pick the features I want to use.
Also please correct any terminology I've misused as I still have no idea what I'm talking about.