Greedy optimization for minimizing the mean squared error.
Works for classification and regression.
Usage
greedyMSE(X, Y, max_iter = 100L)
Arguments
- X
A numeric matrix of features.
- Y
A numeric matrix of target values.
- max_iter
An integer scalar of the maximum number of iterations.
Value
A list with components:
- model_weights
A numeric matrix of model_weights.
- RMSE
A numeric scalar of the root mean squared error.
- max_iter
An integer scalar of the maximum number of iterations.