You can optimize hyper-parameters to improve the performance of your models.
Hyper-Parameter Optimizer Dialog
You can enter information for Hyper-Parameter Optimization in the Settings Panel, and check the results in the Result Panel. Moreover, you can check the state of the Hyper-Parameter Optimizer above the Result Panel.
The state is classified into 5 different states.
Enter Information for Hyper-Parameter Optimizer
To optimize the hyper-parameters, enter the necessary information in the settings panel.
To activate the Run button, you must enter all items in Settings Panel except Early Stopping Rounds.
Your input is not saved after you click the Close button.
|Target Component||Select the component with the target you want to optimize.|
|Target Expression||The last expression of a target component is automatically selected as the target.|
|Target||The Target Expression displays all target values.|
|Sampler||Select one sampler(tuning method) among Bayesian, Grid Search, and Random Search.|
|Type||Assign a type to each target. Choose Min if you want to minimize the target value and Max if you want to maximize it.|
|Iterations||Enter the number of iterations.|
|Early Stopping Rounds||(Optional) Set the number of iterations to stop optimization after a target value no longer needs optimization.|
|Pipeline Parameters||Select one or more pipeline parameters. And enter a Range and choose Distribution for all selected pipeline parameters.|
- Range : Enter a range of the target value.
- Distribution : Choose a distribution among Discrete Uniform, Float, Int, Loguniform, or Uniform.
- Interval : Enter Interval instead of selecting Distribution if Grid Search is selected as Sampler.
Run Hyper-Parameter Optimizer and Apply Results
- If Run is clicked, the hyper-parameter optimization will run from the beginning, regardless of the Ready/Completed/Stopped/Failed status.
- You may have to select one of the Best Trials before clicking Apply if there is more than one target value.
- Click the Run button to run the Hyper-Parameter Optimizer.
- As executions take place, you can view the target value changing in real time in the Graph and Log tabs in the Result Panel.
- Iteration Graph: A real-time graph of target values based on the number of executions
- Log Table: A real-time table of target and parameter values based on the number of executions
- Pareto Front Plot: Graphical representation of the target values (available only when there are two or three target values)
- Parameter Importance: A graph showing the relative importance of various parameters (available only when there are two or more parameters)
- After the hyper-parameter optimization is complete, apply the optimized value by clicking Apply.
Updated 11 days ago