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CS539: Project 3 Zach Pardos.

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Presentation on theme: "CS539: Project 3 Zach Pardos."— Presentation transcript:

1 CS539: Project 3 Zach Pardos

2 Assistments Online Dataset
Math question response data from 592 students. 1,143 math question attributes {correct, incorrect} Average of 200 questions answered per student (lots of missing values) Class: MCAS SCORE {0-29}

3 Assistments Online Dataset
Skill models: 1, 5, 39, 106

4 Assistments Online Dataset
How well can ANNs fit the dataset with only 1, 5, 39 or 106 hidden nodes? Default Weka values used for ANN training Epochs: 500 Learning: 0.3 Momentum: 0.2 No validation set Training-set for testing

5 Assistments Online Dataset
Results for training-set testing: With 1 Hidden Node: Correctly Classified Instances Incorrectly Classified Instances Relative absolute error % With 5 Hidden Nodes: Correctly Classified Instances Incorrectly Classified Instances Relative absolute error %

6 Assistments Online Dataset
Results for training-set testing: With 39 Hidden Nodes: Correctly Classified Instances Incorrectly Classified Instances Relative absolute error % With 106 Hidden Nodes: Correctly Classified Instances Incorrectly Classified Instances Relative absolute error %

7 Assistment Online Dataset
Conclusion: 39 and 106 models predict very well. How well can ANNs generalize and predict instances they haven’t trained on? Next up: 10-fold cross validation

8 Assistment Online Dataset

9 Assistment Online Dataset
Conclusions: ANNs very good at fitting data Not as good at predicting unseen cases Possible that more nodes are required to properly generalize (more CPU!)

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