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.
For more information see [parameters](#agg_functions-stochasticlinearregression-parameters).
``` text
stochasticLogisticRegression(1.0, 1.0, 10, 'SGD')
```
**1.** Fitting
<!---->
See the `Fitting` section in the [stochasticLinearRegression](#stochasticlinearregression-usage-fitting) description.
Predicted labels have to be in \[-1, 1\].
**2.** Predicting
<!---->
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 <1.1ANDans> 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.
- [Difference between linear and logistic regressions.](https://stackoverflow.com/questions/12146914/what-is-the-difference-between-linear-regression-and-logistic-regression)
