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Questions and Answers
Which method involves evaluating all possible combinations of features?
Which method involves evaluating all possible combinations of features?
- Brute Force Method (correct)
- Use Model Coefficients
- Statistical Tests
- Recursive Feature Elimination (RFE)
In the equation $A_{adjusted R^2} = 1-(1-R^2)(n-1)(n-p-1)$, what does 'p' represent?
In the equation $A_{adjusted R^2} = 1-(1-R^2)(n-1)(n-p-1)$, what does 'p' represent?
- Number of features (correct)
- Target variable
- Model coefficients
- Number of samples
What does Recursive Feature Elimination (RFE) do?
What does Recursive Feature Elimination (RFE) do?
- Performs statistical tests like ANOVA or chi-square tests
- Uses model coefficients to determine feature importance
- Evaluates all possible combinations of features
- Fits a model and removes the weakest feature(s) until a specified number of features are left (correct)
What is the significance of larger coefficients (in absolute value) in linear regression?
What is the significance of larger coefficients (in absolute value) in linear regression?
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Why is feature selection important for a model?
Why is feature selection important for a model?
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Study Notes
Feature Selection
- The brute force method involves evaluating all possible combinations of features.
- Recursive Feature Elimination (RFE) is a method that recursively eliminates the weakest feature until a specified number of features is reached.
Linear Regression Coefficients
- Larger coefficients (in absolute value) in linear regression indicate greater importance of the corresponding feature in predicting the target variable.
Model Evaluation
- The adjusted R-squared ($A_{adjusted R^2}$) equation is used to evaluate the goodness of fit of a model, with 'p' representing the number of predictors or features.
- Feature selection is important for a model because it helps prevent overfitting, reduces dimensionality, and improves model interpretability.
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