stochasticLogisticRegression
This function implements stochastic logistic regression. It can be used for binary classification problem, supports the same custom parameters as stochasticLinearRegression and works the same way.
Parameters
Parameters are exactly the same as in stochasticLinearRegression:
learning rate, l2 regularization coefficient, mini-batch size, method for updating weights.
For more information see parameters.
1. Fitting
See the Fitting section in the stochasticLinearRegression description.
Predicted labels have to be in [-1, 1].
2. Predicting
Using saved state we can predict probability of object having label 1.
The query will return a column of probabilities. Note that first argument of evalMLMethod is AggregateFunctionState object, next are columns of features.
We can also set a bound of probability, which assigns elements to different labels.
Then the result will be labels.
test_data is a table like train_data but may not contain target value.
See Also