Model Training
Model training is implemented in the train_model
function. It first tries out several different parameter values in cross
validation, remembers the best parameter value and then uses this value
to train a final model on the full dataset. For further details see the
function documentation of train_model.
Reactive Graph
FastRet is a reactive web app, that means most of its calculations are triggered as reaction to user inputs. The reactive graphs for FastRet’s four modes are shown below, with
- Action buttons shown as red rectangles
- Upload buttons shown as orange rectangles
- Other inputs shown as green rectangles
- Intermediate calculations shown as violet rectangles
- Outputs shown as blue rectangles
- Synchronous functions shown as white rectangles with two lines on the left and right side
- Asynchronous functions shown as hexagons
- Red arrows from one node to another indicating that the first node triggers the second node
- Green arrows from one node to another indicating that the first node holds an input value for the second node
- Blue arrows from one node to another indicating that the first node writes an output value to the second node
