GLMParametersV3 (h2o-java-rest-bindings version 3.8.2.4 API)

java.lang.Object
- water.bindings.pojos.Schema
- - water.bindings.pojos.ModelParametersSchema
  - - water.bindings.pojos.GLMParametersV3

public class GLMParametersV3
extends ModelParametersSchema

Field Summary

Fields
Modifier and Type	Field and Description
`double[]`	`alpha` distribution of regularization between L1 and L2.
`boolean`	`balance_classes` Balance training data class counts via over/under-sampling (for imbalanced data).
`FrameKeyV3`	`beta_constraints` beta constraints
`double`	`beta_epsilon` converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
`float[]`	`class_sampling_factors` Desired over/under-sampling ratios per class (in lexicographic order).
`boolean`	`compute_p_values` request p-values computation, p-values work only with IRLSM solver and no regularization
`GlmGLMModelGLMParametersFamily`	`family` Family.
`double`	`gradient_epsilon` converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver
`java.lang.String[]`	`interactions` A list of predictor column indices to interact.
`boolean`	`intercept` include constant term in the model
`double[]`	`lambda` regularization strength
`double`	`lambda_min_ratio` min lambda used in lambda search, specified as a ratio of lambda_max
`boolean`	`lambda_search` use lambda search starting at lambda max, given lambda is then interpreted as lambda min
`GlmGLMModelGLMParametersLink`	`link`
`int`	`max_active_predictors` Maximum number of active predictors during computation.
`float`	`max_after_balance_size` Maximum relative size of the training data after balancing class counts (can be less than 1.0).
`int`	`max_confusion_matrix_size` Maximum size (# classes) for confusion matrices to be printed in the Logs
`int`	`max_hit_ratio_k` Max.
`int`	`max_iterations` Maximum number of iterations
`DeeplearningDeepLearningModelDeepLearningParametersMissingValuesHandling`	`missing_values_handling` Handling of missing values.
`int`	`nlambdas` number of lambdas to be used in a search
`boolean`	`non_negative` Restrict coefficients (not intercept) to be non-negative
`double`	`obj_reg` likelihood divider in objective value computation, default is 1/nobs
`double`	`objective_epsilon` converge if objective value changes less than this
`double`	`prior` prior probability for y==1.
`boolean`	`remove_collinear_columns` in case of linearly dependent columns remove some of the dependent columns
`GlmGLMModelGLMParametersSolver`	`solver` AUTO will set the solver based on given data and the other parameters.
`boolean`	`standardize` Standardize numeric columns to have zero mean and unit variance
`double`	`tweedie_link_power` Tweedie link power
`double`	`tweedie_variance_power` Tweedie variance power

Fields inherited from class water.bindings.pojos.ModelParametersSchema
checkpoint, fold_assignment, fold_column, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_predictions, max_runtime_secs, model_id, nfolds, offset_column, parallelize_cross_validation, response_column, score_each_iteration, stopping_metric, stopping_rounds, stopping_tolerance, training_frame, validation_frame, weights_column

Constructor Summary

Constructors
Constructor and Description

GLMParametersV3()

Constructors
Constructor and Description
`GLMParametersV3()`

Method Summary

Methods
Modifier and Type Method and Description

java.lang.String toString()
Return the contents of this object as a JSON String.
- Methods inherited from class java.lang.Object
  clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods
Modifier and Type	Method and Description
`java.lang.String`	`toString()` Return the contents of this object as a JSON String.

- Field Detail
  - family
```
public GlmGLMModelGLMParametersFamily family
```
    Family. Use binomial for classification with logistic regression, others are for regression problems.
  - tweedie_variance_power
```
public double tweedie_variance_power
```
    Tweedie variance power
  - tweedie_link_power
```
public double tweedie_link_power
```
    Tweedie link power
  - solver
```
public GlmGLMModelGLMParametersSolver solver
```
    AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Coordinate descent is experimental (beta).
  - alpha
```
public double[] alpha
```
    distribution of regularization between L1 and L2.
  - lambda
```
public double[] lambda
```
    regularization strength
  - lambda_search
```
public boolean lambda_search
```
    use lambda search starting at lambda max, given lambda is then interpreted as lambda min
  - nlambdas
```
public int nlambdas
```
    number of lambdas to be used in a search
  - standardize
```
public boolean standardize
```
    Standardize numeric columns to have zero mean and unit variance
  - missing_values_handling
```
public DeeplearningDeepLearningModelDeepLearningParametersMissingValuesHandling missing_values_handling
```
    Handling of missing values. Either Skip or MeanImputation.
  - non_negative
```
public boolean non_negative
```
    Restrict coefficients (not intercept) to be non-negative
  - max_iterations
```
public int max_iterations
```
    Maximum number of iterations
  - beta_epsilon
```
public double beta_epsilon
```
    converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
  - objective_epsilon
```
public double objective_epsilon
```
    converge if objective value changes less than this
  - gradient_epsilon
```
public double gradient_epsilon
```
    converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver
  - obj_reg
```
public double obj_reg
```
    likelihood divider in objective value computation, default is 1/nobs
  - link
```
public GlmGLMModelGLMParametersLink link
```
  - intercept
```
public boolean intercept
```
    include constant term in the model
  - prior
```
public double prior
```
    prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
  - lambda_min_ratio
```
public double lambda_min_ratio
```
    min lambda used in lambda search, specified as a ratio of lambda_max
  - beta_constraints
```
public FrameKeyV3 beta_constraints
```
    beta constraints
  - max_active_predictors
```
public int max_active_predictors
```
    Maximum number of active predictors during computation. Use as a stopping criterium to prevent expensive model building with many predictors.
  - interactions
```
public java.lang.String[] interactions
```
    A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
  - balance_classes
```
public boolean balance_classes
```
    Balance training data class counts via over/under-sampling (for imbalanced data).
  - class_sampling_factors
```
public float[] class_sampling_factors
```
    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.
  - max_after_balance_size
```
public float max_after_balance_size
```
    Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
  - max_confusion_matrix_size
```
public int max_confusion_matrix_size
```
    Maximum size (# classes) for confusion matrices to be printed in the Logs
  - max_hit_ratio_k
```
public int max_hit_ratio_k
```
    Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
  - compute_p_values
```
public boolean compute_p_values
```
    request p-values computation, p-values work only with IRLSM solver and no regularization
  - remove_collinear_columns
```
public boolean remove_collinear_columns
```
    in case of linearly dependent columns remove some of the dependent columns
- Constructor Detail
  - GLMParametersV3
```
public GLMParametersV3()
```
- Method Detail
  - toString
```
public java.lang.String toString()
```
    Return the contents of this object as a JSON String.
    
    Overrides:
    
    toString in class ModelParametersSchema

Class GLMParametersV3

Field Summary

Fields inherited from class water.bindings.pojos.ModelParametersSchema

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

family

tweedie_variance_power

tweedie_link_power

solver

alpha

lambda

lambda_search

nlambdas

standardize

missing_values_handling

non_negative

max_iterations

beta_epsilon

objective_epsilon

gradient_epsilon

obj_reg

link

intercept

prior

lambda_min_ratio

beta_constraints

max_active_predictors

interactions

balance_classes

class_sampling_factors

max_after_balance_size

max_confusion_matrix_size

max_hit_ratio_k

compute_p_values

remove_collinear_columns

Constructor Detail

GLMParametersV3

Method Detail

toString