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| Hi David, definitely, chapter 11 "Deep Learning Methods" (108 pages...) does exactly that. You can check this PDF sample where the beginning of chapter 11 is present https://t.co/pbYh0wJH0S. Thanks, Etienne |
| Hi Seth, In your case the randomness comes from the distribution learning. (Sometimes, randomness can also come from the algorithm that searches for the mode, but not in this case). Because of the way the automation works (trying various methods,... |
| Dear Terrance, this behavior is strange because the automation is supposed to discard models that give bad probabilities like this, but it can happen in small dataset. Most likely the "RandomForest" method is selected here (you can obtain such... |
| Hi Philip, The ClassifierFunction already has the minimal amount of data needed for prediction. The problem is that some models can be very large (e.g. NearestNeighbors or RandomForest). The first thing you should do is to set the option... |
| In that case, Classify interprets the input as text and uses words as features. Since there is only one word per examples, it won't be able to generalise. What you can do is extract features out of the names. For example you can construct a... |
| Hi Mark, as you suspected in the notebook, this behavior is observed when there is a tie. When the most likely classes have the same probability (or more generally the same utility), a RandomChoice of these classes is done. To avoid this behavior you... |