tiberius data mining Predictive Modelling Software 'attractively simple'   
  Tiberius Data Mining

Building Model

Building Model

Building the model is easy - just press the 'Press to Train' button.


This will be a linear regression model, but logistic regression and neural networks can be created by adding non-linear neurons.


1 . Train ModelTrain Model

This is the button that has to be pressed to both begin and end the learning process.


2 . Learning RateLearning Rate

The algorithm determines in which direction the model 'weights' need to change in order to decrease the error, the learning rate determines the magnitude of the change. Adjust the learning rate while the model is being trained to see the effect of this setting on the plot.


3 . Current ErrorCurrent Error

This displays the current error and the best error to date. The error can actually increase if the learning rate is set too high.


4 . Redisplay RateRedisplay Rate

This setting determines how often the graphs are refreshed.


5 . Graph OnGraph On

Replotting the graph takes up time. If you are not interested in the visuals then deselect this check box.


6 . Reset WeightsReset Weights

This will reset the model weights to a random starting point. This may need to be done if the learning appears to have stalled.


7 . Add neuronsAdd neurons

This will increase the number of hidden neurons in the neural network. More hidden neurons gives the model the ability to learn more complex relationships but also the ability to learn spurious noise.


8 . Training RangeTraining Range

The data can be partitioned into a training and validation set. Pressing this button allows the training data to be specified.

When the model training has been stopped, there is the option to select the model that performed best on the training data or best on the validation data. This is accessed from the neural drop down menu.