CivisML Gradient Boosting Classifier

civis_ml_gradient_boosting_classifier(x, dependent_variable,
  primary_key = NULL, excluded_columns = NULL, loss = c("deviance",
  "exponential"), learning_rate = 0.1, n_estimators = 500,
  subsample = 1, criterion = c("friedman_mse", "mse", "mae"),
  min_samples_split = 2, min_samples_leaf = 1,
  min_weight_fraction_leaf = 0, max_depth = 2,
  min_impurity_split = 1e-07, random_state = 42,
  max_features = "sqrt", max_leaf_nodes = NULL, presort = c("auto",
  TRUE, FALSE), fit_params = NULL, cross_validation_parameters = NULL,
  calibration = NULL, oos_scores_table = NULL, oos_scores_db = NULL,
  oos_scores_if_exists = c("fail", "append", "drop", "truncate"),
  model_name = NULL, cpu_requested = NULL, memory_requested = NULL,
  disk_requested = NULL, notifications = NULL,
  polling_interval = NULL, verbose = FALSE)



See the Data Sources section below.


The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.


Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.


Optional, a vector of columns which will be considered ineligible to be independent variables.


The loss function to be optimized. deviance refers to deviance (logistic regression) for classification with probabilistic outputs. For exponential, gradient boosting recovers the AdaBoost algorithm.


The learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators.


The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.


The fraction of samples to be used for fitting individual base learners. If smaller than 1.0, this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias.


The function to measure the quality of a split. The default value criterion = "friedman_mse" is generally the best as it can provide a better approximation in some cases.


The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.


The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.


The minimum weighted fraction of the sum total of weights required to be at a leaf node.


Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.


Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.


The seed of the random number generator.


The number of features to consider when looking for the best split.


consider max_features at each split.


then max_features is a percentage and max_features * n_features are considered at each split.


then max_features = sqrt(n_features)


then max_features = sqrt(n_features)


then max_features = log2(n_features)


then max_features = n_features


Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.


Whether to presort the data to speed up the finding of best splits in fitting.


Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').


Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.


Optional, if not NULL, calibrate output probabilities with the selected method, sigmoid, or isotonic. Valid only with classification models.


Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".


Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.


Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".


Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.


Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.


Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.


Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.


Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.


Check for job completion every this number of seconds.


Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.


A civis_ml object, a list containing the following elements:


job metadata from scripts_get_custom.


run metadata from scripts_get_custom_runs.


CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.


Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

  • run list, metadata about the run.

  • data list, metadata about the training data.

  • model list, the fitted scikit-learn model with CV results.

  • metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

  • warnings list.

  • data_platform list, training data location.


Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

  • run list, metadata about the run.

  • data list, metadata about the training data.

  • model list, the fitted scikit-learn model.

  • metrics empty list.

  • warnings list.

  • data_platform list, training data location.

Data Sources

For building models with civis_ml, the training data can reside in four different places, a file in the Civis Platform, a CSV or feather-format file on the local disk, a data.frame resident in local the R environment, and finally, a table in the Civis Platform. Use the following helpers to specify the data source when calling civis_ml:


civis_ml(x = df, ...)

local csv file

civis_ml(x = "path/to/data.csv", ...)

file in Civis Platform

civis_ml(x = civis_file(1234))

table in Civis Platform

civis_ml(x = civis_table(table_name = "schema.table", database_name = "database"))


 df <- iris
 names(df) <- gsub("\\.", "_", names(df))

 m <- civis_ml_gradient_boosting_classifier(df,
   dependent_variable = "Species",
   learning_rate = .01,
   n_estimators = 100,
   subsample = .5,
   max_depth = 5,
   max_features = NULL)
 yhat <- fetch_oos_scores(m)

# Grid Search
cv_params <- list(
   n_estimators = c(100, 200, 500),
   learning_rate = c(.01, .1),
   max_depth = c(2, 3))

m <- civis_ml_gradient_boosting_classifier(df,
   dependent_variable = "Species",
   subsample = .5,
   max_features = NULL,
   cross_validation_parameters = cv_params)

pred_info <- predict(m,  civis_table("schema.table", "my_database"),
   output_table = "schema.scores_table")
# }