sean-mcclure/tpot.csv

## tpot.csv

          
            Configuration Name
            Description
            Operators

            
              Default TPOT
              TPOT will search over a broad range of preprocessors, feature constructors, feature selectors, models, and parameters to find a series of operators that minimize the error of the model predictions. Some of these operators are complex and may take a long time to run, especially on larger datasets.
Note: This is the default configuration for TPOT. To use this configuration, use the default value (None) for the config_dict parameter.
              Classification
Regression

            
              TPOT light
              TPOT will search over a restricted range of preprocessors, feature constructors, feature selectors, models, and parameters to find a series of operators that minimize the error of the model predictions. Only simpler and fast-running operators will be used in these pipelines, so TPOT light is useful for finding quick and simple pipelines for a classification or regression problem.
This configuration works for both the TPOTClassifier and TPOTRegressor.
              Classification
Regression

            
              TPOT MDR
              TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here.
Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets.
              Classification
Regression

            
              TPOT sparse
              TPOT uses a configuration dictionary with a one-hot encoder and the operators normally included in TPOT that also support sparse matrices.
This configuration works for both the TPOTClassifier and TPOTRegressor.
              Classification
Regression

            
              TPOT NN
              TPOT uses the same configuration as "Default TPOT" plus additional neural network estimators written in PyTorch (currently only `tpot.builtins.PytorchLRClassifier` and `tpot.builtins.PytorchMLPClassifier`).
Currently only classification is supported, but future releases will include regression estimators.
              Classification

            
              TPOT cuML
              TPOT will search over a restricted configuration using the GPU-accelerated estimators in RAPIDS cuML and DMLC XGBoost. This configuration requires an NVIDIA Pascal architecture or better GPU with compute capability 6.0+, and that the library cuML is installed. With this configuration, all model training and predicting will be GPU-accelerated.
This configuration is particularly useful for medium-sized and larger datasets on which CPU-based estimators are a common bottleneck, and works for both the TPOTClassifier and TPOTRegressor.
              Classification
Regression
Configuration Name	Description	Operators
Default TPOT	TPOT will search over a broad range of preprocessors, feature constructors, feature selectors, models, and parameters to find a series of operators that minimize the error of the model predictions. Some of these operators are complex and may take a long time to run, especially on larger datasets. Note: This is the default configuration for TPOT. To use this configuration, use the default value (None) for the config_dict parameter.	Classification Regression
TPOT light	TPOT will search over a restricted range of preprocessors, feature constructors, feature selectors, models, and parameters to find a series of operators that minimize the error of the model predictions. Only simpler and fast-running operators will be used in these pipelines, so TPOT light is useful for finding quick and simple pipelines for a classification or regression problem. This configuration works for both the TPOTClassifier and TPOTRegressor.	Classification Regression
TPOT MDR	TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here. Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets.	Classification Regression
TPOT sparse	TPOT uses a configuration dictionary with a one-hot encoder and the operators normally included in TPOT that also support sparse matrices. This configuration works for both the TPOTClassifier and TPOTRegressor.	Classification Regression
TPOT NN	TPOT uses the same configuration as "Default TPOT" plus additional neural network estimators written in PyTorch (currently only `tpot.builtins.PytorchLRClassifier` and `tpot.builtins.PytorchMLPClassifier`). Currently only classification is supported, but future releases will include regression estimators.	Classification
TPOT cuML	TPOT will search over a restricted configuration using the GPU-accelerated estimators in RAPIDS cuML and DMLC XGBoost. This configuration requires an NVIDIA Pascal architecture or better GPU with compute capability 6.0+, and that the library cuML is installed. With this configuration, all model training and predicting will be GPU-accelerated. This configuration is particularly useful for medium-sized and larger datasets on which CPU-based estimators are a common bottleneck, and works for both the TPOTClassifier and TPOTRegressor.	Classification Regression