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56 lines
2.0 KiB
Markdown
56 lines
2.0 KiB
Markdown
---
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toc_priority: 222
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---
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# stochasticLogisticRegression {#agg_functions-stochasticlogisticregression}
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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.
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### Parameters {#agg_functions-stochasticlogisticregression-parameters}
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Parameters are exactly the same as in stochasticLinearRegression:
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`learning rate`, `l2 regularization coefficient`, `mini-batch size`, `method for updating weights`.
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For more information see [parameters](#agg_functions-stochasticlinearregression-parameters).
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``` text
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stochasticLogisticRegression(1.0, 1.0, 10, 'SGD')
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```
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**1.** Fitting
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See the `Fitting` section in the [stochasticLinearRegression](#stochasticlinearregression-usage-fitting) description.
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Predicted labels have to be in \[-1, 1\].
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**2.** Predicting
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Using saved state we can predict probability of object having label `1`.
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``` sql
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WITH (SELECT state FROM your_model) AS model SELECT
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evalMLMethod(model, param1, param2) FROM test_data
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```
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The query will return a column of probabilities. Note that first argument of `evalMLMethod` is `AggregateFunctionState` object, next are columns of features.
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We can also set a bound of probability, which assigns elements to different labels.
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``` sql
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SELECT ans < 1.1 AND ans > 0.5 FROM
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(WITH (SELECT state FROM your_model) AS model SELECT
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evalMLMethod(model, param1, param2) AS ans FROM test_data)
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```
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Then the result will be labels.
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`test_data` is a table like `train_data` but may not contain target value.
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**See Also**
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- [stochasticLinearRegression](../../../sql-reference/aggregate-functions/reference/stochasticlinearregression.md#agg_functions-stochasticlinearregression)
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- [Difference between linear and logistic regressions.](https://stackoverflow.com/questions/12146914/what-is-the-difference-between-linear-regression-and-logistic-regression)
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