Initialize an object of ArffLoader.
Retrieve this object’s structure and set it’s class index
(The feature that needs to be predicted –
Iteratively retrieve an instance from the training set
and update the classifier ( updateClassifier() ).
Evaluate the trained model against the test dataset.
Step to train an
Stochastic gradient descent is a gradient descent
optimization method for minimizing an objective
function that is written as a sum of differentiable
Applicable to large datasets, since each iteration
involves processing only a single instance of the
Stochastic Gradient Descent
w: Parameter to be estimated.
Qi(w): A single instance of data
SGD class does not support Numeric data types,
unless it is configured to use Huber Loss or Square
The learning rate should not be too small (Slow
process) or large (Overshoot the minimum).
Some errors had to be resolved by consulting the
WEKA Java code.