Random Forest Classifier Training Jobs

id - stringrequired

The ID for this Spark job. Used in the API to reference this job. Allowed characters: a-z, A-Z, dash (-) and underscore (_)

<= 128 characters

Match pattern: ^[A-Za-z0-9_\-]+$

trainingCollection - stringrequired

Solr Collection containing labeled training data

>= 1 characters

fieldToVectorize - stringrequired

Solr field containing text training data. Data from multiple fields with different weights can be combined by specifying them as field1:weight1,field2:weight2 etc.

>= 1 characters

dataFormat - string

Spark-compatible format which training data comes in (like 'solr', 'hdfs', 'file', 'parquet' etc)

Default: solr

Allowed values: solrhdfsfileparquet

trainingDataFrameConfigOptions - object

Additional spark dataframe loading configuration options

trainingDataFilterQuery - string

Solr query to use when loading training data

>= 3 characters

Default: *:*

trainingDataSamplingFraction - number

Fraction of the training data to use

<= 1

exclusiveMaximum: false

Default: 1

randomSeed - integer

For any deterministic pseudorandom number generation

Default: 1234

outputCollection - string

Solr Collection to store model-labeled data to

sourceFields - string

Solr fields to load (comma-delimited). Leave empty to allow the job to select the required fields to load at runtime.

modelId - string

Identifier for the model to be trained; uses the supplied Spark Job ID if not provided.

>= 1 characters

analyzerConfig - string

LuceneTextAnalyzer schema for tokenization (JSON-encoded)

Default: { "analyzers": [{ "name": "StdTokLowerStop","charFilters": [ { "type": "htmlstrip" } ],"tokenizer": { "type": "standard" },"filters": [{ "type": "lowercase" },{ "type": "KStem" },{ "type": "length", "min": "2", "max": "32767" },{ "type": "fusionstop", "ignoreCase": "true", "format": "snowball", "words": "org/apache/lucene/analysis/snowball/english_stop.txt" }] }],"fields": [{ "regex": ".+", "analyzer": "StdTokLowerStop" } ]}

withIdf - boolean

Weight vector components based on inverse document frequency

Default: true

w2vDimension - integer

Word-vector dimensionality to represent text (choose > 0 to use)

exclusiveMinimum: false

Default: 0

w2vWindowSize - integer

The window size (context words from [-window, window]) for word2vec

>= 3

exclusiveMinimum: false

Default: 5

w2vMaxSentenceLength - integer

Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to `maxSentenceLength` size.

>= 3

exclusiveMinimum: false

Default: 1000

w2vMaxIter - integer

Maximum number of iterations of the word2vec training

Default: 1

w2vStepSize - number

Training parameter for word2vec convergence (change at your own peril)

>= 0.005

exclusiveMinimum: false

Default: 0.025

minDF - number

To be kept, terms must occur in at least this number of documents (if > 1.0), or at least this fraction of documents (if <= 1.0)

Default: 0

maxDF - number

To be kept, terms must occur in no more than this number of documents (if > 1.0), or no more than this fraction of documents (if <= 1.0)

Default: 1

norm - integer

p-norm to normalize vectors with (choose -1 to turn normalization off)

Default: 2

Allowed values: -1012

predictedLabelField - string

Solr field which will contain labels when classifier is applied to documents

Default: labelPredictedByFusionModel

minSparkPartitions - integer

Minimum number of Spark partitions for training job.

>= 1

exclusiveMinimum: false

Default: 200

trainingLabelField - stringrequired

Solr field containing labels for training instances (should be single-valued strings)

gridSearch - boolean

Perform grid search to optimize hyperparameters

Default: false

evaluationMetricType - string

Optimize hyperparameter search over one of [binary, multiclass, regression] metrics, or 'none'

Default: none

Allowed values: binarymulticlassregressionnone

autoBalanceClasses - boolean

Ensure that all classes of training data have the same size

Default: true

minTrainingSamplesPerClass - integer

Ensure that all classes of training data have at least this many examples

>= 1

exclusiveMinimum: false

Default: 100

makeOtherClass - boolean

Create a label class 'Other' which contains all examples not in a class large enough to train on

Default: true

otherClassName - string

Label class name for the catch-all 'Other' class

>= 1 characters

Default: Other

maxDepth - integer

Maximum depth of the tree (>= 0). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

>= 1

<= 20

exclusiveMinimum: false

exclusiveMaximum: false

Default: 5

maxBins - integer

Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.

<= 128

exclusiveMinimum: false

exclusiveMaximum: false

Default: 32

numTrees - integer

Number of trees to train (>= 1)

>= 1

<= 1000

exclusiveMinimum: false

exclusiveMaximum: false

Default: 20

type - stringrequired

Default: random_forests_classifier

Allowed values: random_forests_classifier