GBM === Supported HTTP methods and descriptions --------------------------------------- URL --- http://:/GBM.json Input parameters ---------------- * **classification**, a boolean, expert Do classification or regression. Since version 1 * **validation**, a Frame, expert Validation frame. Since version 1 * **n_folds**, a int, expert Number of folds for cross-validation (if no validation data is specified). Since version 1 * **keep_cross_validation_splits**, a boolean, expert Keep cross-validation dataset splits. Since version 1 * **ntrees**, a int, critical Number of trees. Since version 1 * **max_depth**, a int, critical Maximum tree depth. Since version 1 * **min_rows**, a int, secondary Fewest allowed observations in a leaf (in R called 'nodesize'). Since version 1 * **nbins**, a int, secondary Build a histogram of this many bins, then split at the best point. Since version 1 * **score_each_iteration**, a boolean, expert Perform scoring after each iteration (can be slow). Since version 1 * **importance**, a boolean, expert Compute variable importance (true/false).. Since version 1 * **balance_classes**, a boolean, expert Balance training data class counts via over/under-sampling (for imbalanced data). Since version 1 * **max_after_balance_size**, a float, expert Maximum relative size of the training data after balancing class counts (can be less than 1.0). Since version 1 * **checkpoint**, a Key, expert Model checkpoint to start building a new model from. Since version 1 * **overwrite_checkpoint**, a boolean, expert Overwrite checkpoint. Since version 1 * **family**, a Family, critical Distribution for computing loss function. AUTO selects gaussian for continuous and multinomial for categorical response. Since version 1 * **learn_rate**, a double, secondary Learning rate, from 0. to 1.0. Since version 1 * **grid_parallelism**, a int, secondary Grid search parallelism. Since version 1 * **seed**, a long, expert Seed for the random number generator - only for balancing classes (autogenerated). Since version 1 Output JSON elements -------------------- * **xval_models**, a Key[] Cross-validation models. Since version 1, expert * **_distribution**, a long[] Class distribution. Since version 1, expert HTTP response codes ------------------- 200 OK Success and error responses are identical.