Update Feature Selection.md

data-science-notes/notes/Data Processing/Feature Selection.md

@@ -9,8 +9,17 @@ updated: 2023-01-20T23:04:48-08:00
 # Feature Selection
 
 ## Summary
+Feature selection is a critical step in the data science process, as it involves identifying the most important variables or features that are relevant to predicting a particular outcome. Here is a summary of the key points to keep in mind about feature selection:
+
+-Feature selection involves choosing a subset of the available features that are most relevant to the outcome variable.
+-The goal of feature selection is to reduce the dimensionality of the data, which can improve model performance, reduce overfitting, and simplify interpretation.
+-There are three main approaches to feature selection: filter methods, wrapper methods, and embedded methods.
+-Filter methods involve ranking features based on statistical tests or other metrics and selecting the top features.
+-Wrapper methods involve evaluating different feature subsets using a machine learning algorithm and selecting the best performing subset.
+-Embedded methods incorporate feature selection into the model building process, by optimizing the feature subset during training.
+-It is important to carefully evaluate the performance of the selected features using appropriate validation techniques, as overfitting can occur if the feature selection process is not properly validated.
+-Different feature selection methods may be appropriate for different types of data and modeling tasks, and there is often a trade-off between model complexity and performance.
 
----
 
 ## Related Topics
 
@@ -22,4 +31,4 @@ updated: 2023-01-20T23:04:48-08:00
 
 ## Footnotes
 
----
+---