Classification pipelines
- examples.simple.classification.classification_pipelines.cnn_composite_pipeline(composite_flag=True)[source]
Returns pipeline with the following structure:
Where cnn - convolutional neural network, rf - random forest
- Parameters
composite_flag (bool) – add additional random forest estimator
- Return type
fedot.core.pipelines.pipeline.Pipeline
- examples.simple.classification.classification_pipelines.classification_pipeline_with_balancing(custom_params=None)[source]
Returns pipeline with the following structure:
Where resample - algorithm for balancing dataset, logit - logistic_regression
- Parameters
custom_params – custom parameters for resample node
- examples.simple.classification.classification_pipelines.classification_pipeline_without_balancing()[source]
Returns: pipeline with the following structure:
Where logit - logistic_regression
- examples.simple.classification.classification_pipelines.classification_complex_pipeline()[source]
Returns pipeline with the following structure:
- examples.simple.classification.classification_pipelines.classification_random_forest_pipeline()[source]
Returns pipeline with the following structure:
- examples.simple.classification.classification_pipelines.classification_isolation_forest_pipeline()[source]
Returns pipeline with the following structure:
- examples.simple.classification.classification_pipelines.classification_svc_complex_pipeline()[source]
Returns pipeline with the following structure:
Where svc - support vector classifier, logit - logistic regression, knn - K nearest neighbors classifier, rf - random forest classifier
