Help with Forward Stepwise Selection Algorithm
Below is a simplified forward stepwise selection algorithm and I am trying to understand the logic in step 2.
Algorithm:
- Let $M_0$ denote the null model, which contains no predictors. For
- For $k=0,....,p-1:$
- (a) Consider all $p-k$ models that augment the predictors in $M_k$ with one additional predictor.
- (b) Choose the best among these $p-k$ models, and call it $M_{k+1}$. Here best is defined as having smallest RSS or highest $R^2$.
- Select a single best model from among $M_0,....M_p$ using cross-validated prediction error, $C_p$ (AIC), BIC, or adjusted $R^2$.
In step 2(a), what is the significance of $p-k$? My understanding is since there are $p$ predictors, total number of possible models should be $2^p$. If we are iterating $k$, and in the first iteration where $k=0$, shouldn't we consider all the remaining models which would be $2^p-1$? Why are we considering $p-k$?
statistics machine-learning
add a comment |
Below is a simplified forward stepwise selection algorithm and I am trying to understand the logic in step 2.
Algorithm:
- Let $M_0$ denote the null model, which contains no predictors. For
- For $k=0,....,p-1:$
- (a) Consider all $p-k$ models that augment the predictors in $M_k$ with one additional predictor.
- (b) Choose the best among these $p-k$ models, and call it $M_{k+1}$. Here best is defined as having smallest RSS or highest $R^2$.
- Select a single best model from among $M_0,....M_p$ using cross-validated prediction error, $C_p$ (AIC), BIC, or adjusted $R^2$.
In step 2(a), what is the significance of $p-k$? My understanding is since there are $p$ predictors, total number of possible models should be $2^p$. If we are iterating $k$, and in the first iteration where $k=0$, shouldn't we consider all the remaining models which would be $2^p-1$? Why are we considering $p-k$?
statistics machine-learning
add a comment |
Below is a simplified forward stepwise selection algorithm and I am trying to understand the logic in step 2.
Algorithm:
- Let $M_0$ denote the null model, which contains no predictors. For
- For $k=0,....,p-1:$
- (a) Consider all $p-k$ models that augment the predictors in $M_k$ with one additional predictor.
- (b) Choose the best among these $p-k$ models, and call it $M_{k+1}$. Here best is defined as having smallest RSS or highest $R^2$.
- Select a single best model from among $M_0,....M_p$ using cross-validated prediction error, $C_p$ (AIC), BIC, or adjusted $R^2$.
In step 2(a), what is the significance of $p-k$? My understanding is since there are $p$ predictors, total number of possible models should be $2^p$. If we are iterating $k$, and in the first iteration where $k=0$, shouldn't we consider all the remaining models which would be $2^p-1$? Why are we considering $p-k$?
statistics machine-learning
Below is a simplified forward stepwise selection algorithm and I am trying to understand the logic in step 2.
Algorithm:
- Let $M_0$ denote the null model, which contains no predictors. For
- For $k=0,....,p-1:$
- (a) Consider all $p-k$ models that augment the predictors in $M_k$ with one additional predictor.
- (b) Choose the best among these $p-k$ models, and call it $M_{k+1}$. Here best is defined as having smallest RSS or highest $R^2$.
- Select a single best model from among $M_0,....M_p$ using cross-validated prediction error, $C_p$ (AIC), BIC, or adjusted $R^2$.
In step 2(a), what is the significance of $p-k$? My understanding is since there are $p$ predictors, total number of possible models should be $2^p$. If we are iterating $k$, and in the first iteration where $k=0$, shouldn't we consider all the remaining models which would be $2^p-1$? Why are we considering $p-k$?
statistics machine-learning
statistics machine-learning
asked Dec 3 '18 at 0:12
DoLare
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