tying up loose ends. understand your data no answers available, only data
Post on 27-Dec-2015
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Probability of Y given XOr the most likely Y given XCollaborative Filtering – people who
like X probably like Y
Probability of Y given XOr the most likely Y given XCollaborative Filtering – people who
like X probably like YNeural Networks – input X triggers Y
output (behaviorism)
Goal is predictionClassification is a type of association Includes pattern recognition: OCR,
faces, diagnosis, speech, NLP…
Goal is predictionClassification is a type of association Includes pattern recognition: OCR,
faces, diagnosis, speech, NLP… Includes compression
If the output is a continuous numberEx. Automatic steering
inputs: sensors (video, GPS, proximity…)
output: degree of rotation of the wheel Ex. ALVINN
Different algorithms use different error calculations
Simplest : # wrong / # total ie. 2/5 = .4 or 40%
Different algorithms use different error calculations
Simplest : # wrong / # total ie. 2/5 = .4 or 40%
Other examples: WER Mean Squared Error
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput Validation
Training
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
Fold 1
Fold 2
Fold 3
Fold 4
Fold 5
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
Fold 1
Fold 2
Fold 3
Fold 4
Fold 5
Train
Test
-> Learner 1 error = .01
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
Fold 1
Fold 2
Fold 3
Fold 5
Fold 4
Train
Test
-> Learner 2 error = .012
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
InputInput OutputOutputInputInput OutputOutput
Fold 1
Fold 2
Fold 3
Fold 5
Fold 3
Train
Test
-> Learner 3 error = .011
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