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Given a large enough training data set the tree will learn to distinguish between even and odd numbers. Only on numbers that occur frequently enough in the training dataset. An example in R because a plot will explain best: # example data x <- sample(1:6, size = 1000, replace = TRUE) y <- ifelse(x%%2, 1, 0) xmpl <- data.frame(x = x, y = y) # ...


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If your training accuracy is not changing at all during training, your model parameters are probably not changing at all either. That is normally due to an error in code rather than a model problem.


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It is often said on this site that R-squared is problematic for nonlinear models, search ... , but I cannot find a canonic thread. A published paper, with simulations, is this, and they also point to information measures like AIC as better alternatives. From an answer in this R-help thread There is a good reason that an nls model fit in R does not ...


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You’ll use two new binary features and use one hot encoding. For example, for Dr. A your features will be [1,0], and for Dr. B your features will be [0,1]. Assigning arbitrary numbers to each doctor is not the correct approach because it induces an implicit ordering, i.e. normally you don’t have Dr. A < Dr. B but depending on your assigned numbers, you’ll ...


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You can use any metric you want. The best metric to use depends on the data you have. You can consider using the F1 score. Depending on the averaging technique you use, you can nudge things towards reducing false negatives. If you are using python/sklearn, you can pick your averaging method in the argument (https://scikit-learn.org/stable/modules/generated/...


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