InfogramParametersV3 (h2o-bindings 3.46.0 API)

java.lang.Object
- water.bindings.pojos.SchemaV3
- - water.bindings.pojos.ModelParametersSchemaV3
  - - water.bindings.pojos.InfogramParametersV3

public class InfogramParametersV3
extends ModelParametersSchemaV3

Field Summary

Fields
Modifier and Type	Field and Description
`InfogramAlgorithm`	`algorithm` Type of machine learning algorithm used to build the infogram.
`java.lang.String`	`algorithmParams` Customized parameters for the machine learning algorithm specified in the algorithm parameter.
`boolean`	`balanceClasses` Balance training data class counts via over/under-sampling (for imbalanced data).
`float[]`	`classSamplingFactors` Desired over/under-sampling ratios per class (in lexicographic order).
`double`	`dataFraction` The fraction of training frame to use to build the infogram model.
`float`	`maxAfterBalanceSize` Maximum relative size of the training data after balancing class counts (can be less than 1.0).
`int`	`maxIterations` Maximum number of iterations.
`double`	`netInformationThreshold` A number between 0 and 1 representing a threshold for net information, defaulting to 0.1.
`FrameKeyV3`	`plugValues` Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues).
`double`	`prior` Prior probability for y==1.
`java.lang.String[]`	`protectedColumns` Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if applicable.
`double`	`relevanceIndexThreshold` A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1.
`double`	`safetyIndexThreshold` A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1.
`long`	`seed` Seed for pseudo random number generator (if applicable).
`boolean`	`standardize` Standardize numeric columns to have zero mean and unit variance.
`int`	`topNFeatures` An integer specifying the number of columns to evaluate in the infogram.
`double`	`totalInformationThreshold` A number between 0 and 1 representing a threshold for total information, defaulting to 0.1.

Constructor Summary

Constructors
Constructor and Description

InfogramParametersV3()
Public constructor

Constructors
Constructor and Description
`InfogramParametersV3()` Public constructor

Method Summary

All Methods Instance Methods Concrete 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

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

- Field Detail
  - seed
```
public long seed
```
    Seed for pseudo random number generator (if applicable).
  - standardize
```
public boolean standardize
```
    Standardize numeric columns to have zero mean and unit variance.
  - plugValues
```
@SerializedName(value="plug_values")
public FrameKeyV3 plugValues
```
    Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues).
  - maxIterations
```
@SerializedName(value="max_iterations")
public int maxIterations
```
    Maximum number of iterations.
  - 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.
  - balanceClasses
```
@SerializedName(value="balance_classes")
public boolean balanceClasses
```
    Balance training data class counts via over/under-sampling (for imbalanced data).
  - classSamplingFactors
```
@SerializedName(value="class_sampling_factors")
public float[] classSamplingFactors
```
    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.
  - maxAfterBalanceSize
```
@SerializedName(value="max_after_balance_size")
public float maxAfterBalanceSize
```
    Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
  - algorithm
```
public InfogramAlgorithm algorithm
```
    Type of machine learning algorithm used to build the infogram. Options include 'AUTO' (gbm), 'deeplearning' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM with default parameters), or 'xgboost' (if available, XGBoost with default parameters).
  - algorithmParams
```
@SerializedName(value="algorithm_params")
public java.lang.String algorithmParams
```
    Customized parameters for the machine learning algorithm specified in the algorithm parameter.
  - protectedColumns
```
@SerializedName(value="protected_columns")
public java.lang.String[] protectedColumns
```
    Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if applicable. These features (e.g. race, gender, etc) should not drive the prediction of the response.
  - totalInformationThreshold
```
@SerializedName(value="total_information_threshold")
public double totalInformationThreshold
```
    A number between 0 and 1 representing a threshold for total information, defaulting to 0.1. For a specific feature, if the total information is higher than this threshold, and the corresponding net information is also higher than the threshold ``net_information_threshold``, that feature will be considered admissible. The total information is the x-axis of the Core Infogram. Default is -1 which gets set to 0.1.
  - netInformationThreshold
```
@SerializedName(value="net_information_threshold")
public double netInformationThreshold
```
    A number between 0 and 1 representing a threshold for net information, defaulting to 0.1. For a specific feature, if the net information is higher than this threshold, and the corresponding total information is also higher than the total_information_threshold, that feature will be considered admissible. The net information is the y-axis of the Core Infogram. Default is -1 which gets set to 0.1.
  - relevanceIndexThreshold
```
@SerializedName(value="relevance_index_threshold")
public double relevanceIndexThreshold
```
    A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1. This is only used when ``protected_columns`` is set by the user. For a specific feature, if the relevance index value is higher than this threshold, and the corresponding safety index is also higher than the safety_index_threshold``, that feature will be considered admissible. The relevance index is the x-axis of the Fair Infogram. Default is -1 which gets set to 0.1.
  - safetyIndexThreshold
```
@SerializedName(value="safety_index_threshold")
public double safetyIndexThreshold
```
    A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1. This is only used when protected_columns is set by the user. For a specific feature, if the safety index value is higher than this threshold, and the corresponding relevance index is also higher than the relevance_index_threshold, that feature will be considered admissible. The safety index is the y-axis of the Fair Infogram. Default is -1 which gets set to 0.1.
  - dataFraction
```
@SerializedName(value="data_fraction")
public double dataFraction
```
    The fraction of training frame to use to build the infogram model. Defaults to 1.0, and any value greater than 0 and less than or equal to 1.0 is acceptable.
  - topNFeatures
```
@SerializedName(value="top_n_features")
public int topNFeatures
```
    An integer specifying the number of columns to evaluate in the infogram. The columns are ranked by variable importance, and the top N are evaluated. Defaults to 50.
- Constructor Detail
  - InfogramParametersV3
```
public InfogramParametersV3()
```
    Public constructor
- Method Detail
  - toString
```
public java.lang.String toString()
```
    Return the contents of this object as a JSON String.
    
    Overrides:
    
    toString in class ModelParametersSchemaV3

Class InfogramParametersV3

Field Summary

Fields inherited from class water.bindings.pojos.ModelParametersSchemaV3

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

seed

standardize

plugValues

maxIterations

prior

balanceClasses

classSamplingFactors

maxAfterBalanceSize

algorithm

algorithmParams

protectedColumns

totalInformationThreshold

netInformationThreshold

relevanceIndexThreshold

safetyIndexThreshold

dataFraction

topNFeatures

Constructor Detail

InfogramParametersV3

Method Detail

toString