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98 lines
3.4 KiB
Markdown
98 lines
3.4 KiB
Markdown
---
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toc_priority: 64
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toc_title: Machine Learning
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---
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# Machine Learning Functions {#machine-learning-functions}
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## evalMLMethod {#machine_learning_methods-evalmlmethod}
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Prediction using fitted regression models uses `evalMLMethod` function. See link in `linearRegression`.
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## stochasticLinearRegressionn {#stochastic-linear-regression}
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The [stochasticLinearRegression](../../sql-reference/aggregate-functions/reference/stochasticlinearregression.md#agg_functions-stochasticlinearregression) aggregate function implements stochastic gradient descent method using linear model and MSE loss function. Uses `evalMLMethod` to predict on new data.
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## stochasticLogisticRegression {#stochastic-logistic-regression}
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The [stochasticLogisticRegression](../../sql-reference/aggregate-functions/reference/stochasticlogisticregression.md#agg_functions-stochasticlogisticregression) aggregate function implements stochastic gradient descent method for binary classification problem. Uses `evalMLMethod` to predict on new data.
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## bayesAB {#bayesab}
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Compares test groups (variants) and calculates for each group the probability to be the best one. The first group is used as a control group.
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**Syntax**
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``` sql
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bayesAB(distribution_name, higher_is_better, variant_names, x, y)
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```
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**Arguments**
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- `distribution_name` — Name of the probability distribution. [String](../../sql-reference/data-types/string.md). Possible values:
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- `beta` for [Beta distribution](https://en.wikipedia.org/wiki/Beta_distribution)
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- `gamma` for [Gamma distribution](https://en.wikipedia.org/wiki/Gamma_distribution)
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- `higher_is_better` — Boolean flag. [Boolean](../../sql-reference/data-types/boolean.md). Possible values:
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- `0` - lower values are considered to be better than higher
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- `1` - higher values are considered to be better than lower
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- `variant_names` - Variant names. [Array](../../sql-reference/data-types/array.md)([String](../../sql-reference/data-types/string.md)).
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- `x` - Numbers of tests for the corresponding variants. [Array](../../sql-reference/data-types/array.md)([Float64](../../sql-reference/data-types/float.md)).
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- `y` - Numbers of successful tests for the corresponding variants. [Array](../../sql-reference/data-types/array.md)([Float64](../../sql-reference/data-types/float.md)).
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!!! note "Note"
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All three arrays must have the same size. All `x` and `y` values must be non-negative constant numbers. `y` cannot be larger than `x`.
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**Returned values**
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For each variant the function calculates:
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- `beats_control` - long-term probability to out-perform the first (control) variant
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- `to_be_best` - long-term probability to out-perform all other variants
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Type: JSON.
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**Example**
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Query:
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``` sql
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SELECT bayesAB('beta', 1, ['Control', 'A', 'B'], [3000., 3000., 3000.], [100., 90., 110.]) FORMAT PrettySpace;
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```
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Result:
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``` text
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{
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"data":[
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{
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"variant_name":"Control",
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"x":3000,
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"y":100,
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"beats_control":0,
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"to_be_best":0.22619
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},
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{
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"variant_name":"A",
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"x":3000,
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"y":90,
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"beats_control":0.23469,
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"to_be_best":0.04671
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},
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{
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"variant_name":"B",
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"x":3000,
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"y":110,
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"beats_control":0.7580899999999999,
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"to_be_best":0.7271
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}
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]
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}
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```
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[Original article](https://clickhouse.tech/docs/en/query_language/functions/machine-learning-functions/) <!--hide-->
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