CLASSIFICATION AND PREDICTION - UniversitÃ¤t Wien

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Peter Brezany Institut für Softwarewissenschaft, WS 2002 4 Tab. 1: Training Set Example Feature Description Value Slide 7 Sales_Price Months_Ago Num_Apartments Year_Built Plumbing_Fixtures Heating_Type Basement_Garage Attached_Garage Living_Area Deck_Area Porch_Area Recroom_Area Basement_Area the sales price of the house When it was sold? Number of dwelling units Year built Number of plumbing fixtures Heating system type Basement garage (number of cars) Attached frame garage area (in square feet) Total living are (squaer feet) Deck / open porch area(square feet) Enclosed poech area(sqare feet) Recreation room area (square feet) Finished basement area (square feet) $ 171 ,000 4 1 1923 9 A 0 120 1,614 0 210 0 175 Tab. 2: Massaged Values for Training Set Example Feature Range of Values Original Value Massaged Value Slide 8 Sales_Price Months_Ago Num_Apartments Year_Built Plumbing_Fixtures Heating_Type Basement_Garage Attached_Garage Living_Area Deck_Area Porch_Area Recroom_Area Basement_Area $ 103,000-$ 250,000 0-23 1-3 1850-1986 5-17 Coded as A or B 0-2 0-228 714-4185 0-738 0-452 0-672 0-810 $ 171,000 4 1 1923 9 B 0 120 1,614 0 210 0 175 0.4626 0.1739 0.0000 0.5328 0.3333 1.0000 0.0000 0.5263 0.2593 0.0000 0.4646 0.0000 0.2160
Peter Brezany Institut für Softwarewissenschaft, WS 2002 5 How to use a NN? 1. Training The NN is ready to be trained when the training examples have all been massaged. Slide 9 During the training phase, we repeatedly feed the examples in the training set though the NN. The NN compares its predicted output value to the actual sales price and adjust all its internal weights to improve the prediction. By going through all the training examples (sometimes many times), the NN calculates a good set of weights. Training is complete when the weights no longer change very much or until the network has gone through the training set a maximum number of times. 2. Test (Evaluating) We run the network on a test set that it had never seen before - when the performance is satisfactory, then we have a neural network model. The model is ready for use. How to use a NN? (2) 3. Production runs The NN model takes descriptive information about a house, suitable massaged, and produces an output. Slide 10 One problem: the output is a number between 0 and 1, so we need to unmassage the value to turn it back into a sales price. If we get a value like 0.75, then we multiply it by the size of the range ($147,000) and then add the base number in the range ($103,00) to get an appraisal value of $213,250. WARNING A neural network is only as good as the training set used to generate it. The model is static and must be explicitly updated by adding more recent examples into the training set and retraining the network (or training a new network) in order to keep it up-to-date and useful.
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CLASSIFICATION AND PREDICTION - UniversitÃ¤t Wien

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