```
library(sparklyr)
<- spark_connect(master = "local")
sc <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)
mtcars_tbl
<- mtcars_tbl %>%
partitions sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
<- partitions$training
mtcars_training <- partitions$test
mtcars_test
# Specify the grid
<- c("gaussian", "gamma", "poisson")
family <- c("identity", "log")
link <- expand.grid(family = family, link = link, stringsAsFactors = FALSE)
family_link <- data.frame(family_link, rmse = 0)
family_link
# Train the models
for (i in seq_len(nrow(family_link))) {
<- mtcars_training %>%
glm_model ml_generalized_linear_regression(mpg ~ .,
family = family_link[i, 1],
link = family_link[i, 2]
)
<- ml_predict(glm_model, mtcars_test)
pred 3] <- ml_regression_evaluator(pred, label_col = "mpg")
family_link[i,
}
family_link #> family link rmse
#> 1 gaussian identity 2.881163
#> 2 gamma identity 2.954531
#> 3 poisson identity 2.942684
#> 4 gaussian log 2.613220
#> 5 gamma log 2.721447
#> 6 poisson log 2.676343
```

# Spark ML – Generalized Linear Regression

*R/ml_regression_generalized_linear_regression.R*

## ml_generalized_linear_regression

## Description

Perform regression using Generalized Linear Model (GLM).

## Usage

```
ml_generalized_linear_regression(
x, formula = NULL,
family = "gaussian",
link = NULL,
fit_intercept = TRUE,
offset_col = NULL,
link_power = NULL,
link_prediction_col = NULL,
reg_param = 0,
max_iter = 25,
weight_col = NULL,
solver = "irls",
tol = 1e-06,
variance_power = 0,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
uid = random_string("generalized_linear_regression_"),
... )
```

## Arguments

Arguments | Description |
---|---|

x | A `spark_connection` , `ml_pipeline` , or a `tbl_spark` . |

formula | Used when `x` is a `tbl_spark` . R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details. |

family | Name of family which is a description of the error distribution to be used in the model. Supported options: “gaussian”, “binomial”, “poisson”, “gamma” and “tweedie”. Default is “gaussian”. |

link | Name of link function which provides the relationship between the linear predictor and the mean of the distribution function. See for supported link functions. |

fit_intercept | Boolean; should the model be fit with an intercept term? |

offset_col | Offset column name. If this is not set, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0. |

link_power | Index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R “statmod” package. |

link_prediction_col | Link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction. |

reg_param | Regularization parameter (aka lambda) |

max_iter | The maximum number of iterations to use. |

weight_col | The name of the column to use as weights for the model fit. |

solver | Solver algorithm for optimization. |

tol | Param for the convergence tolerance for iterative algorithms. |

variance_power | Power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively. |

features_col | Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by `ft_r_formula` . |

label_col | Label column name. The column should be a numeric column. Usually this column is output by `ft_r_formula` . |

prediction_col | Prediction column name. |

uid | A character string used to uniquely identify the ML estimator. |

… | Optional arguments; see Details. |

## Details

When `x`

is a `tbl_spark`

and `formula`

(alternatively, `response`

and `features`

) is specified, the function returns a `ml_model`

object wrapping a `ml_pipeline_model`

which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument `predicted_label_col`

(defaults to `"predicted_label"`

) can be used to specify the name of the predicted label column. In addition to the fitted `ml_pipeline_model`

, `ml_model`

objects also contain a `ml_pipeline`

object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by `ml_save`

with `type = "pipeline"`

to faciliate model refresh workflows. Valid link functions for each family is listed below. The first link function of each family is the default one.

gaussian: “identity”, “log”, “inverse”

binomial: “logit”, “probit”, “loglog”

poisson: “log”, “identity”, “sqrt”

gamma: “inverse”, “identity”, “log”

tweedie: power link function specified through

`link_power`

. The default link power in the tweedie family is`1 - variance_power`

.

## Value

The object returned depends on the class of `x`

.

`spark_connection`

: When`x`

is a`spark_connection`

, the function returns an instance of a`ml_estimator`

object. The object contains a pointer to a Spark`Predictor`

object and can be used to compose`Pipeline`

objects.`ml_pipeline`

: When`x`

is a`ml_pipeline`

, the function returns a`ml_pipeline`

with the predictor appended to the pipeline.`tbl_spark`

: When`x`

is a`tbl_spark`

, a predictor is constructed then immediately fit with the input`tbl_spark`

, returning a prediction model.`tbl_spark`

, with`formula`

: specified When`formula`

is specified, the input`tbl_spark`

is first transformed using a`RFormula`

transformer before being fit by the predictor. The object returned in this case is a`ml_model`

which is a wrapper of a`ml_pipeline_model`

.

## Examples

## See Also

See https://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms. Other ml algorithms: `ml_aft_survival_regression()`

, `ml_decision_tree_classifier()`

, `ml_gbt_classifier()`

, `ml_isotonic_regression()`

, `ml_linear_regression()`

, `ml_linear_svc()`

, `ml_logistic_regression()`

, `ml_multilayer_perceptron_classifier()`

, `ml_naive_bayes()`

, `ml_one_vs_rest()`

, `ml_random_forest_classifier()`