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 are exactly the same as in stochasticLinearRegression:
l2 regularization coefficient,
method for updating weights.
For more information see parameters.
stochasticLogisticRegression(1.0, 1.0, 10, 'SGD')
See the `Fitting` section in the [stochasticLinearRegression](#stochasticlinearregression-usage-fitting) description. Predicted labels have to be in \[-1, 1\].
Using saved state we can predict probability of object having label `1`. ``` sql WITH (SELECT state FROM your_model) AS model SELECT evalMLMethod(model, param1, param2) FROM test_data ``` 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. ``` sql SELECT ans < 1.1 AND ans > 0.5 FROM (WITH (SELECT state FROM your_model) AS model SELECT evalMLMethod(model, param1, param2) AS ans FROM test_data) ``` Then the result will be labels. `test_data` is a table like `train_data` but may not contain target value.