LION COMMUNITY USAGE CASE

Neural networks: prediction of house prices.

 

Data:

each row in the matrix is a house, characterized by a series of parameters like: median value in dollars, number of rooms, age, crime rate in town, nitric oxides concentration, accessibility to radial highways, etc.

Objectives of neural network modelling:

  1. To build a model associating selected inputs (for example number of rooms, age, crime rate) to one output, for example the house value. The model can then be used to predict house values for new houses.
  2. To estimate the prediction expected prediction error of the neural network model.
  3. To identify which inputs are more relevant to predict the output.
  4. To quickly analyze how the output depends on selected inputs by using the LIONoso Output Sweeper tool.

LIONoso sample visualization: neural network training and testing

The form of the model, the input(s) and the output to be predicted are selected. Then a training session is started and the obtained training and testing error during training are immediately visualized. One can easily experiment with different architectures and save the best predictor when finished. In the image below one sees a steady decrease of the training error (red curve), while the test error first decreases and then reaches a plateau. Continuing training after reaching this plateau can in some cases lead to over-training (memorization of the training data with poor generalization, see manual for additional details).

LIONoso 2.1 visualization a data mining image

LIONoso sample visualization: Output Sweeper

After a neural network model is built, the LIONoso Output Sweeper tool can be used for visualizing the output when the various inputs are changing. Two input variables are fixed as X and Y coordinates, the output is shown with different colors. The value of the other input variables can be fixed by the corresponding slider. A specified input variable can be selected for an automated sweep over all possible values, showing a moving slice of the input space.

LIONoso 2.1 visualization a data mining image

LIONoso workbench connectivity

The data objects, network factory, produced neural network, and visualizations present in the LIONoso workbench.

LIONoso 2.1 workbench for neural networks

Download the LIONoso-ready data file: neural-network.lion