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mn-mikke/gbm-model-EnumLimited.json

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Sparkling Water Examples
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gbm-model-EnumLimited.json
{
"__meta": {
"schema_version": 3,
"schema_name": "GBMModelV3",
"schema_type": "GBMModel"
},
"model_id": {
"__meta": {
"schema_version": 3,
"schema_name": "ModelKeyV3",
"schema_type": "Key<Model>"
},
"name": "GBM_model_Java_1599228606828_1",
"type": "Key<Model>",
"URL": "/3/Models/GBM_model_Java_1599228606828_1"
},
"algo": "gbm",
"algo_full_name": "Gradient Boosting Machine",
"response_column_name": "CAPSULE",
"data_frame": {
"__meta": {
"schema_version": 3,
"schema_name": "FrameKeyV3",
"schema_type": "Key<Frame>"
},
"name": "frame_rdd_25317020589",
"type": "Key<Frame>",
"URL": "/3/Frames/frame_rdd_25317020589"
},
"timestamp": 1599228644142,
"have_pojo": true,
"have_mojo": true,
"parameters": [
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "model_id",
"label": "model_id",
"help": "Destination id for this model; auto-generated if not specified.",
"required": false,
"type": "Key<Model>",
"default_value": null,
"actual_value": {
"__meta": {
"schema_version": 3,
"schema_name": "ModelKeyV3",
"schema_type": "Key<Model>"
},
"name": "GBM_model_Java_1599228606828_1",
"type": "Key<Model>",
"URL": "/3/Models/GBM_model_Java_1599228606828_1"
},
"input_value": null,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "training_frame",
"label": "training_frame",
"help": "Id of the training data frame.",
"required": false,
"type": "Key<Frame>",
"default_value": null,
"actual_value": {
"__meta": {
"schema_version": 3,
"schema_name": "FrameKeyV3",
"schema_type": "Key<Frame>"
},
"name": "frame_rdd_25317020589",
"type": "Key<Frame>",
"URL": "/3/Frames/frame_rdd_25317020589"
},
"input_value": {
"__meta": {
"schema_version": 3,
"schema_name": "FrameKeyV3",
"schema_type": "Key<Frame>"
},
"name": "frame_rdd_25317020589",
"type": "Key<Frame>",
"URL": "/3/Frames/frame_rdd_25317020589"
},
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "validation_frame",
"label": "validation_frame",
"help": "Id of the validation data frame.",
"required": false,
"type": "Key<Frame>",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nfolds",
"label": "nfolds",
"help": "Number of folds for K-fold cross-validation (0 to disable or >= 2).",
"required": false,
"type": "int",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "keep_cross_validation_models",
"label": "keep_cross_validation_models",
"help": "Whether to keep the cross-validation models.",
"required": false,
"type": "boolean",
"default_value": true,
"actual_value": true,
"input_value": true,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "keep_cross_validation_predictions",
"label": "keep_cross_validation_predictions",
"help": "Whether to keep the predictions of the cross-validation models.",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "keep_cross_validation_fold_assignment",
"label": "keep_cross_validation_fold_assignment",
"help": "Whether to keep the cross-validation fold assignment.",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "score_each_iteration",
"label": "score_each_iteration",
"help": "Whether to score during each iteration of model training.",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "score_tree_interval",
"label": "score_tree_interval",
"help": "Score the model after every so many trees. Disabled if set to 0.",
"required": false,
"type": "int",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "fold_assignment",
"label": "fold_assignment",
"help": "Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems.",
"required": false,
"type": "enum",
"default_value": "AUTO",
"actual_value": null,
"input_value": "AUTO",
"level": "secondary",
"values": [
"AUTO",
"Random",
"Modulo",
"Stratified"
],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "fold_column",
"label": "fold_column",
"help": "Column with cross-validation fold index assignment per observation.",
"required": false,
"type": "VecSpecifier",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [
"training_frame"
],
"is_mutually_exclusive_with": [
"response_column",
"weights_column",
"ignored_columns",
"offset_column"
],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "response_column",
"label": "response_column",
"help": "Response variable column.",
"required": false,
"type": "VecSpecifier",
"default_value": null,
"actual_value": {
"__meta": {
"schema_version": 3,
"schema_name": "ColSpecifierV3",
"schema_type": "VecSpecifier"
},
"column_name": "CAPSULE",
"is_member_of_frames": null
},
"input_value": {
"__meta": {
"schema_version": 3,
"schema_name": "ColSpecifierV3",
"schema_type": "VecSpecifier"
},
"column_name": "CAPSULE",
"is_member_of_frames": null
},
"level": "critical",
"values": [],
"is_member_of_frames": [
"training_frame",
"validation_frame"
],
"is_mutually_exclusive_with": [
"weights_column",
"ignored_columns",
"offset_column",
"fold_column"
],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "ignored_columns",
"label": "ignored_columns",
"help": "Names of columns to ignore for training.",
"required": false,
"type": "string[]",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "critical",
"values": [],
"is_member_of_frames": [
"training_frame",
"validation_frame"
],
"is_mutually_exclusive_with": [
"response_column",
"weights_column",
"offset_column",
"fold_column"
],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "ignore_const_cols",
"label": "ignore_const_cols",
"help": "Ignore constant columns.",
"required": false,
"type": "boolean",
"default_value": true,
"actual_value": true,
"input_value": true,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "offset_column",
"label": "offset_column",
"help": "Offset column. This will be added to the combination of columns before applying the link function.",
"required": false,
"type": "VecSpecifier",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [
"training_frame",
"validation_frame"
],
"is_mutually_exclusive_with": [
"response_column",
"weights_column",
"ignored_columns",
"fold_column"
],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "weights_column",
"label": "weights_column",
"help": "Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.",
"required": false,
"type": "VecSpecifier",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [
"training_frame",
"validation_frame"
],
"is_mutually_exclusive_with": [
"response_column",
"ignored_columns",
"offset_column",
"fold_column"
],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "balance_classes",
"label": "balance_classes",
"help": "Balance training data class counts via over/under-sampling (for imbalanced data).",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "class_sampling_factors",
"label": "class_sampling_factors",
"help": "Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.",
"required": false,
"type": "float[]",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_after_balance_size",
"label": "max_after_balance_size",
"help": "Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.",
"required": false,
"type": "float",
"default_value": 5,
"actual_value": 5,
"input_value": 5,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_confusion_matrix_size",
"label": "max_confusion_matrix_size",
"help": "[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs",
"required": false,
"type": "int",
"default_value": 20,
"actual_value": 20,
"input_value": 20,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_hit_ratio_k",
"label": "max_hit_ratio_k",
"help": "Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)",
"required": false,
"type": "int",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "ntrees",
"label": "ntrees",
"help": "Number of trees.",
"required": false,
"type": "int",
"default_value": 50,
"actual_value": 50,
"input_value": 50,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_depth",
"label": "max_depth",
"help": "Maximum tree depth (0 for unlimited).",
"required": false,
"type": "int",
"default_value": 5,
"actual_value": 5,
"input_value": 5,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "min_rows",
"label": "min_rows",
"help": "Fewest allowed (weighted) observations in a leaf.",
"required": false,
"type": "double",
"default_value": 10,
"actual_value": 10,
"input_value": 10,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nbins",
"label": "nbins",
"help": "For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point",
"required": false,
"type": "int",
"default_value": 20,
"actual_value": 20,
"input_value": 20,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nbins_top_level",
"label": "nbins_top_level",
"help": "For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level",
"required": false,
"type": "int",
"default_value": 1024,
"actual_value": 1024,
"input_value": 1024,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nbins_cats",
"label": "nbins_cats",
"help": "For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.",
"required": false,
"type": "int",
"default_value": 1024,
"actual_value": 1024,
"input_value": 1024,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "r2_stopping",
"label": "r2_stopping",
"help": "r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this",
"required": false,
"type": "double",
"default_value": 1.7976931348623157e+308,
"actual_value": 1.7976931348623157e+308,
"input_value": 1.7976931348623157e+308,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "stopping_rounds",
"label": "stopping_rounds",
"help": "Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)",
"required": false,
"type": "int",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "stopping_metric",
"label": "stopping_metric",
"help": "Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.",
"required": false,
"type": "enum",
"default_value": "AUTO",
"actual_value": null,
"input_value": "AUTO",
"level": "secondary",
"values": [
"AUTO",
"deviance",
"logloss",
"MSE",
"RMSE",
"MAE",
"RMSLE",
"AUC",
"AUCPR",
"lift_top_group",
"misclassification",
"mean_per_class_error",
"anomaly_score",
"custom",
"custom_increasing"
],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "stopping_tolerance",
"label": "stopping_tolerance",
"help": "Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)",
"required": false,
"type": "double",
"default_value": 0.001,
"actual_value": 0.001,
"input_value": 0.001,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_runtime_secs",
"label": "max_runtime_secs",
"help": "Maximum allowed runtime in seconds for model training. Use 0 to disable.",
"required": false,
"type": "double",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "seed",
"label": "seed",
"help": "Seed for pseudo random number generator (if applicable)",
"required": false,
"type": "long",
"default_value": -1,
"actual_value": 1,
"input_value": 1,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "build_tree_one_node",
"label": "build_tree_one_node",
"help": "Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "learn_rate",
"label": "learn_rate",
"help": "Learning rate (from 0.0 to 1.0)",
"required": false,
"type": "double",
"default_value": 0.1,
"actual_value": 0.1,
"input_value": 0.1,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "learn_rate_annealing",
"label": "learn_rate_annealing",
"help": "Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) ",
"required": false,
"type": "double",
"default_value": 1,
"actual_value": 1,
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Raw
gbm-model.json
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"input_value": 20,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_hit_ratio_k",
"label": "max_hit_ratio_k",
"help": "Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)",
"required": false,
"type": "int",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "ntrees",
"label": "ntrees",
"help": "Number of trees.",
"required": false,
"type": "int",
"default_value": 50,
"actual_value": 2,
"input_value": 2,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_depth",
"label": "max_depth",
"help": "Maximum tree depth (0 for unlimited).",
"required": false,
"type": "int",
"default_value": 5,
"actual_value": 5,
"input_value": 5,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "min_rows",
"label": "min_rows",
"help": "Fewest allowed (weighted) observations in a leaf.",
"required": false,
"type": "double",
"default_value": 10,
"actual_value": 10,
"input_value": 10,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nbins",
"label": "nbins",
"help": "For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point",
"required": false,
"type": "int",
"default_value": 20,
"actual_value": 20,
"input_value": 20,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nbins_top_level",
"label": "nbins_top_level",
"help": "For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level",
"required": false,
"type": "int",
"default_value": 1024,
"actual_value": 1024,
"input_value": 1024,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "nbins_cats",
"label": "nbins_cats",
"help": "For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.",
"required": false,
"type": "int",
"default_value": 1024,
"actual_value": 1024,
"input_value": 1024,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "r2_stopping",
"label": "r2_stopping",
"help": "r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this",
"required": false,
"type": "double",
"default_value": 1.7976931348623157e+308,
"actual_value": 1.7976931348623157e+308,
"input_value": 1.7976931348623157e+308,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "stopping_rounds",
"label": "stopping_rounds",
"help": "Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)",
"required": false,
"type": "int",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "stopping_metric",
"label": "stopping_metric",
"help": "Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.",
"required": false,
"type": "enum",
"default_value": "AUTO",
"actual_value": null,
"input_value": "AUTO",
"level": "secondary",
"values": [
"AUTO",
"deviance",
"logloss",
"MSE",
"RMSE",
"MAE",
"RMSLE",
"AUC",
"AUCPR",
"lift_top_group",
"misclassification",
"mean_per_class_error",
"anomaly_score",
"custom",
"custom_increasing"
],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "stopping_tolerance",
"label": "stopping_tolerance",
"help": "Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)",
"required": false,
"type": "double",
"default_value": 0.001,
"actual_value": 0.001,
"input_value": 0.001,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_runtime_secs",
"label": "max_runtime_secs",
"help": "Maximum allowed runtime in seconds for model training. Use 0 to disable.",
"required": false,
"type": "double",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "seed",
"label": "seed",
"help": "Seed for pseudo random number generator (if applicable)",
"required": false,
"type": "long",
"default_value": -1,
"actual_value": 1,
"input_value": 1,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "build_tree_one_node",
"label": "build_tree_one_node",
"help": "Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "learn_rate",
"label": "learn_rate",
"help": "Learning rate (from 0.0 to 1.0)",
"required": false,
"type": "double",
"default_value": 0.1,
"actual_value": 0.1,
"input_value": 0.1,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "learn_rate_annealing",
"label": "learn_rate_annealing",
"help": "Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) ",
"required": false,
"type": "double",
"default_value": 1,
"actual_value": 1,
"input_value": 1,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "distribution",
"label": "distribution",
"help": "Distribution function",
"required": false,
"type": "enum",
"default_value": "AUTO",
"actual_value": "gaussian",
"input_value": "AUTO",
"level": "secondary",
"values": [
"AUTO",
"bernoulli",
"quasibinomial",
"ordinal",
"multinomial",
"gaussian",
"poisson",
"gamma",
"tweedie",
"laplace",
"quantile",
"huber",
"custom"
],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "quantile_alpha",
"label": "quantile_alpha",
"help": "Desired quantile for Quantile regression, must be between 0 and 1.",
"required": false,
"type": "double",
"default_value": 0.5,
"actual_value": 0.5,
"input_value": 0.5,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "tweedie_power",
"label": "tweedie_power",
"help": "Tweedie power for Tweedie regression, must be between 1 and 2.",
"required": false,
"type": "double",
"default_value": 1.5,
"actual_value": 1.5,
"input_value": 1.5,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "huber_alpha",
"label": "huber_alpha",
"help": "Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).",
"required": false,
"type": "double",
"default_value": 0.9,
"actual_value": 0.9,
"input_value": 0.9,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "checkpoint",
"label": "checkpoint",
"help": "Model checkpoint to resume training with.",
"required": false,
"type": "Key<Model>",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "sample_rate",
"label": "sample_rate",
"help": "Row sample rate per tree (from 0.0 to 1.0)",
"required": false,
"type": "double",
"default_value": 1,
"actual_value": 1,
"input_value": 1,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "sample_rate_per_class",
"label": "sample_rate_per_class",
"help": "A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree",
"required": false,
"type": "double[]",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "col_sample_rate",
"label": "col_sample_rate",
"help": "Column sample rate (from 0.0 to 1.0)",
"required": false,
"type": "double",
"default_value": 1,
"actual_value": 1,
"input_value": 1,
"level": "critical",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "col_sample_rate_change_per_level",
"label": "col_sample_rate_change_per_level",
"help": "Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)",
"required": false,
"type": "double",
"default_value": 1,
"actual_value": 1,
"input_value": 1,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "col_sample_rate_per_tree",
"label": "col_sample_rate_per_tree",
"help": "Column sample rate per tree (from 0.0 to 1.0)",
"required": false,
"type": "double",
"default_value": 1,
"actual_value": 1,
"input_value": 1,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "min_split_improvement",
"label": "min_split_improvement",
"help": "Minimum relative improvement in squared error reduction for a split to happen",
"required": false,
"type": "double",
"default_value": 0.00001,
"actual_value": 0.00001,
"input_value": 0.00001,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "histogram_type",
"label": "histogram_type",
"help": "What type of histogram to use for finding optimal split points",
"required": false,
"type": "enum",
"default_value": "AUTO",
"actual_value": "UniformAdaptive",
"input_value": "AUTO",
"level": "secondary",
"values": [
"AUTO",
"UniformAdaptive",
"Random",
"QuantilesGlobal",
"RoundRobin"
],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "max_abs_leafnode_pred",
"label": "max_abs_leafnode_pred",
"help": "Maximum absolute value of a leaf node prediction",
"required": false,
"type": "double",
"default_value": 1.7976931348623157e+308,
"actual_value": 1.7976931348623157e+308,
"input_value": 1.7976931348623157e+308,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "pred_noise_bandwidth",
"label": "pred_noise_bandwidth",
"help": "Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions",
"required": false,
"type": "double",
"default_value": 0,
"actual_value": 0,
"input_value": 0,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "categorical_encoding",
"label": "categorical_encoding",
"help": "Encoding scheme for categorical features",
"required": false,
"type": "enum",
"default_value": "AUTO",
"actual_value": "Enum",
"input_value": "AUTO",
"level": "secondary",
"values": [
"AUTO",
"Enum",
"OneHotInternal",
"OneHotExplicit",
"Binary",
"Eigen",
"LabelEncoder",
"SortByResponse",
"EnumLimited"
],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": true
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "calibrate_model",
"label": "calibrate_model",
"help": "Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.",
"required": false,
"type": "boolean",
"default_value": false,
"actual_value": false,
"input_value": false,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "calibration_frame",
"label": "calibration_frame",
"help": "Calibration frame for Platt Scaling",
"required": false,
"type": "Key<Frame>",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "custom_metric_func",
"label": "custom_metric_func",
"help": "Reference to custom evaluation function, format: `language:keyName=funcName`",
"required": false,
"type": "string",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "custom_distribution_func",
"label": "custom_distribution_func",
"help": "Reference to custom distribution, format: `language:keyName=funcName`",
"required": false,
"type": "string",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "export_checkpoints_dir",
"label": "export_checkpoints_dir",
"help": "Automatically export generated models to this directory.",
"required": false,
"type": "string",
"default_value": null,
"actual_value": null,
"input_value": null,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "monotone_constraints",
"label": "monotone_constraints",
"help": "A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.",
"required": false,
"type": "KeyValue[]",
"default_value": null,
"actual_value": [],
"input_value": [],
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "check_constant_response",
"label": "check_constant_response",
"help": "Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.",
"required": false,
"type": "boolean",
"default_value": true,
"actual_value": true,
"input_value": true,
"level": "expert",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
},
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelParameterSchemaV3",
"schema_type": "Iced"
},
"name": "gainslift_bins",
"label": "gainslift_bins",
"help": "Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.",
"required": false,
"type": "int",
"default_value": -1,
"actual_value": -1,
"input_value": -1,
"level": "secondary",
"values": [],
"is_member_of_frames": [],
"is_mutually_exclusive_with": [],
"gridable": false
}
],
"output": {
"__meta": {
"schema_version": 3,
"schema_name": "GBMModelOutputV3",
"schema_type": "GBMOutput"
},
"names": [
"CAPSULE",
"RACE",
"DPROS",
"DCAPS",
"PSA",
"VOL",
"GLEASON",
"AGE"
],
"original_names": null,
"column_types": [
"Numeric",
"Numeric",
"Numeric",
"Numeric",
"Numeric",
"Numeric",
"Numeric",
"Numeric"
],
"domains": [
null,
null,
null,
null,
null,
null,
null,
null
],
"cross_validation_models": [
{
"__meta": {
"schema_version": 3,
"schema_name": "ModelKeyV3",
"schema_type": "Key<Model>"
},
"name": "GBM_model_Java_1599220346478_1_cv_1",
"type": "Key<Model>",
"URL": "/3/Models/GBM_model_Java_1599220346478_1_cv_1"
},
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Raw
kmeans-model.json
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