Train or predict a logistic regression model on streaming data.
Training uses Stochastic Gradient Descent to update the model based on
each new batch of incoming data from a DStream.
Each batch of data is assumed to be an RDD of LabeledPoints.
The number of data points per batch can vary, but the number
of features must be constant. An initial weight
vector must be provided.
New in version 1.5.0.
Step size for each iteration of gradient descent.
Number of iterations run for each batch of data.
Fraction of each batch of data to use for updates.
L2 Regularization parameter.
Value used to determine when to terminate iterations.
Returns the latest model.
Use the model to make predictions on batches of data from a DStream.
Use the model to make predictions on the values of a DStream and carry over its keys.
Set the initial value of weights.
Train the model on the incoming dstream.
Use the model to make predictions on batches of data from a
DStream containing predictions.
Use the model to make predictions on the values of a DStream and
carry over its keys.
This must be set before running trainOn and predictOn.