What are variable reducing techniques

Question

Accepted Answer

Variable reducing techniques are methods used to identify and select the most relevant variables in a dataset.

Variable reducing techniques help in reducing the number of variables in a dataset.
These techniques aim to identify the most important variables that contribute significantly to the outcome.
Some common variable reducing techniques include feature selection, dimensionality reduction, and correlation analysis.
Feature selection methods like backward elimination, forward selection, and stepwise regression help in selecting the most relevant variables.
Dimensionality reduction techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) reduce the number of variables while preserving important information.
Correlation analysis helps in identifying variables that are highly correlated with the outcome variable and can be used for variable selection.

Accepted Answer

We can drop the variable based on missing value frequency, quartile distribution, variance, catagorical variable with levels more then 12 can be dropped, etc. Based on IV we can further reduce the variables

What are variable reducing techniques

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