Parameter Fitter

Find the best hyper-parameters for a model.

Inputs

• Data: input data
• Learner: learning algorithm

Parameter fitter shows performance of a learning algorithms with different settings of a hyper-parameter. The widget is currently limited to a single integer parameter. Not all learning algorithms support hyper-parameter tuning.

1. Choose the parameter to fit.
2. Define the lower and the upper limit; step size is determined automatically.
3. Alternatively, specifies the values for the parameter. The widget also accepts ..., e.g. 1, 2, 3, ..., 10 or 40, 60, ..., 100. When the parameter has a minimal value (e.g. the number of components cannot be negative), one can also omit the lower bound, e.g. ..., 80, 100; and if the parameter has a maximal value, one can omit the upper bound, e.g. 2, 4, 6, ...,.
4. A plot showing the performance at different values of the parameter. The graph shows AUC for classification problems and R2 for regression.