By setting AutoTune during model training, hyperparameters are automatically explored and optimized.
Optimization methods supported by VARISTA
The following methods are supported by VARISTA.
- Grid Search
- Radamized Search
Modeling › Templates (tab) › Any Template or New Template › Model Settings section
Set the parameter setting item AutoTune as desired.
(In this example, TPE Optimization with Optuna is selected.)
Set a range for any hyperparameter you wish to explore.
The format for input is as follows.
- The range is separated by commas (e.g. 0.000, 1.0)
It is also possible to enter the range in E notation (e.g. 1e-8,1.0).
- If you want to fix the value during the search, enter an arbitrary number.
- Leave blank for default value
When you have completed the settings, save the template under an arbitrary name.
Training with AutoTune
Modeling › Models (tab) › New Model
Start creating a new model using the template you have just created.
Check the optimization history
Select a model that has completed training to view the model report.
To check the optimization history, do the following
Algorithm › Select any algorithm › AutoTune (tab)
The best trial rounds will be displayed in green text color.