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November 7, 2021 00:18
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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 |
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