For example, we have a dataset, and we want to find best representative hyperplane for this dataset. In other words, we aim to perform regression operation.
This hyperplane can be in linear, sinusoidal or logistic format.
How can I determine this ?
How can we understand which model is the best one for our dataset ?
This is very important because if the data samples are distributed nonlinearly, we cannot choose a linear model.
The first answer to this question which comes to my mind is plotting the samples. In this way, I can see the distribution pattern of our data samples, and I can decide my learner function
(f = sin(x) or f = sigmoid(x))
However, if we have more than 2 features, we cannot plot our data samples. This means that plotting is not a good solution for model selection and understanding the data.
Is there any other solution for understanding the samples distribution and determining model function (learner function f) ?