feature extraction, selection, and classification concepts

Question

I know that support vector machine, random tree forest and logistic regression are famous machine learning (ML)algorithms for classification.

I'm confused the terminology between a feature extraction, selection and classification.

Does the above ML algorithms are used for extracting features not part of selecting?

Does the ML algorithms include both process of feature extraction and classification?

Does the result of training the ML algorithm (accuracy, specificity, sensitivity..) tell us the result of classifying a disease after the feature extraction?

Answer 1

Regarding your confusion about the 3 terminologies,

Feature extraction: When you want to create new features out of raw data (say you have the transaction_day column but you are only interested in the month, so you create a new column "transaction_month" out of "transaction_day")

Feature selection: You have many features but want to select only the important ones (how many of them is another topic to be studied). This could speed up the process of learning and with the right strategy, you would not sacrifice accuracy in many applications.

Classification: Is a family of supervised (labeled) machine learning that your goal is to assign observations to known classes (for example emails to spam or normal class)

Note: Some of machine learning algorithms like "Lasso" have build-in feature selection but for others, large coefficient of the feature after training usually shows the importance of the feature (read more about recursive feature elimination (rfe))

you may also find a good discussion in this post.