AutoGluon Learner (TabularPredictor)

Creates ML Model using python package AutoGluon

https://auto.gluon.ai/stable/index.html

Quick Prototyping
Build machine learning solutions on raw data in a few lines of code.

State-of-the-art Techniques
Automatically utilize SOTA models without expert knowledge.

Creates ML model using python package AutoGloun.

https://auto.gluon.ai/stable/index.html

Easy to Deploy
Move from experimentation to production with cloud predictors and pre-built containers.

Customizable
Extensible with custom feature processing, models, and metrics.

Options

Optimize for deployment

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Ignore Columns

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Target Column:

Time Limit:

seconds

Problem Type:

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Training Preset:

Evaluation Metric:

Defaults:%%00010Classification = 'accuracy'%%00010Regression = 'root_mean_squared_error'%%00010%%00010Other options:%%00010Classifiction:%%00010‘accuracy’, ‘balanced_accuracy’, ‘f1’, ‘f1_macro’, ‘f1_micro’, ‘f1_weighted’, ‘roc_auc’, ‘roc_auc_ovo_macro’, ‘average_precision’, ‘precision’, ‘precision_macro’, ‘precision_micro’, ‘precision_weighted’, ‘recall’, ‘recall_macro’, ‘recall_micro’, ‘recall_weighted’, ‘log_loss’, ‘pac_score’%%00010%%00010Regression:%%00010[‘root_mean_squared_error’, ‘mean_squared_error’, ‘mean_absolute_error’, ‘median_absolute_error’, ‘mean_absolute_percentage_error’, ‘r2’]

Nodes

• Rule Engine Variable6 ×
• CASE Switch End3 ×
• CASE Switch Start3 ×
• Single Selection Configuration2 ×
• Boolean Configuration1 ×
• Show all 30 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Conda Integration
• FeatureKNIME Javasnippet
• FeatureKNIME Quick Forms

Links

• No links available