-- Oct 11 In-Class Exercise
Hypothesis: There exists a set of hyperparameters and trained weights that can accurately classify the images on Zener cards given sufficient number of examples and time to train.
Description:
Test cases for the hypothesis would scan the following properties:
• Varying training set size (how much ground truth does the model need?)
• Varying classification letter (are some easier to learn than others?)
• Vary the hyperparameter epsilon to see how increasing or decreasing it alters the model (how much leeway do we have for increased accuracy before overfitting, or reduced runtime without failure to find the maximal separator?)
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Edited: 2017-10-15)
Hypothesis: There exists a set of hyperparameters and trained weights that can accurately classify the images on Zener cards given sufficient number of examples and time to train.
Description:
Test cases for the hypothesis would scan the following properties:
• Varying training set size (how much ground truth does the model need?)
• Varying classification letter (are some easier to learn than others?)
• Vary the hyperparameter epsilon to see how increasing or decreasing it alters the model (how much leeway do we have for increased accuracy before overfitting, or reduced runtime without failure to find the maximal separator?)