io.github.timsetsfire.datarobot
advanced options of the project.
advanced options of the project. See io.github.timsetsfire.datarobot.AdvancedOptions.
0 if autopiloe, 2 is manual
0 if autopiloe, 2 is manual
Name of returns project
Max wait time
Returns a copy (post-EDA1) copy of the project.
add maxWait appropriate to request happening behind the scenes.
list of io.github.timsetsfire.datarobot.Model to use for blending
Returns io.github.timsetsfire.datarobot.ModelJob for the requested blender
Create feature list within a project.
Create feature list within a project. see also io.github.timsetsfire.datarobot.Featurelist.createFeaturelist
Name for new featurelist
List of feature names to include in list
new feature list object io.github.timsetsfire.datarobot.Featurelist
project creation time
project creation time
Returns Unit. Deletes projects
the name of the dataset used to create the project
the name of the dataset used to create the project
list of users and permissions for given project
List of models to blend together
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blueprint id
blueprint
blueprint d
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blueprint d
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returns a list of eligible blueprints
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return io.github.timsetsfire.datarobot.Feature given specified featurename
association matrix metric
association matric value type
returns feature association matric for a given featurelist
Returns associtation matric details for feature1
and feature2
.
all featurelists available in project
set of all features available in project
model id to get
frozen model for specified id
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list of frozen models for project
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set of metrics available if given featureName
is set as target
model id to get
model for specified modelId
list of models for the project
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Returns model recommended for deployment
REturns a list of models recommended by DataRobot
blueprint d
implement this
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true if holdout has been unlocked
true if holdout has been unlocked
project id
project id
the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data
the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data
the maximum number of rows of the dataset that can be used to suc- cessfully train a model without going into the validation data
the maximum number of rows of the dataset that can be used to suc- cessfully train a model without going into the validation data
the metric used to select the best-performing models
the metric used to select the best-performing models
partition of given project.
partition of given project. See io.github.timsetsfire.datarobot.Partition.
for binary classification projects, the class designated to be the positive class.
for binary classification projects, the class designated to be the positive class. Otherwise, null.
project name
project name
Caluclated assocation matrix for specific featurelist
Caluclated assocation matrix for specific featurelist
the maximum percentage of the dataset that can be used to successfully train a scaleout model without going into the validation data.
the maximum percentage of the dataset that can be used to successfully train a scaleout model without going into the validation data. May exceed maxTrainPct, in which case only scaleout models can be trained up to this point.
the maximum number of rows of the dataset that can used be used to successfully train a scaleout model without going into the validation data.
the maximum number of rows of the dataset that can used be used to successfully train a scaleout model without going into the validation data. May exceed maxTrainRows, in which case only scaleout models can be trained up to this point.
Start modeling
Start modeling
Number of workers to use for modeling
project target
project target
either Regression, Binary, or Multiclass
either Regression, Binary, or Multiclass
(Since version ) see corresponding Javadoc for more information.