What is the main purpose of Feature Selection? I have a small medical dataset (200 samples) that contains only 6 cases of the condition I am trying to predict using machine learning. So far, the dataset is not proving useful for predicting the target variable and is resulting in models with 0% recall and precision, probably due to the scarcity of the minority class.
However, in order to learn from the dataset, I applied Feature Selection techniques to deduct what features are useful in predicting the target variable and see if this supports or contradicts previous literature on the matter.
When I reran my models using the reduced dataset, this still resulted in 0% recall and precision. So the prediction performance has not improved using feature selection. But the features returned by the applying Feature Selection have given me more insight into the data.
So my question is, is the purpose of Feature Selection:

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*to improve prediction performance

*or can the purpose be identifying relevant features in the prediction and learning more about the dataset

So in other words, is Feature Selection just a tool to achieve improved performance, or can it be an end in itself?

In short, both answers are correct.
Feature selection has two main purposes:

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*It reduces the number of features in the dataset. This reduces the model training time and reduces the chance of overfitting.

*It helps you understand the data i.e. which features in the dataset are the most important.

Hence, I would not expect feature selection to help when training your model, unless you are overfitting the training data.
