Feature Selection
Feature Selection chooses a set of variables that will be present in the data records used for model training, testing and prediction processing. Feature selection is a crucial process in AI modeling that involves choosing the most relevant and informative features (variables or attributes) from a dataset to improve model performance, efficiency, and interpretability.
Factors
Factors to consider for feature selection include:
relevance: features should be correlated with the predictive objectives of the model
redundancy: redundancy of features should be minimized
dimensionality: manages the number of features
availability: features should be available in a sufficient number of model training records
accuracy: features should provide data that is as accurate as possible
Process
The feature selection process includes these elements:
data: data can be from sources such as databases, files, commercial repositories
prediction objectives: can include factors such as applications, accuracy
feature selection functions: is an iterative process of mining, modeling, and analysis which is carried out until the prediction objectives are met
mining functions: includes functions such as data discovery, data cleaning, data normalization, data ETL
modeling functions: includes functions such as model training, feature selection, model selection, hyperparameter tuning
analysis functions: includes functions such as probability and statistical analysis, business analysis, model accuracy analysis, prediction results analysis