scikit-learn expects 2d num arrays for the training dataset for a fit function. The dataset you are passing in is a 3d array you need to reshape the array into a 2d. nsamples, nx, ny = train_dataset.shape d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))
You can do this using the epi package in R, however I could not find similar package or example in Python. The optimal cut off point would be where “true positive rate” is high and the “false positive rate” is low. Based on this logic, I have pulled an example below to find optimal threshold. … Read more
Your y is of type object, so sklearn cannot recognize its type. Add the line y=y.astype(‘int’) right after the line y = train[:, 1].
Preliminary facts In functional sense, the sigmoid is a partial case of the softmax function, when the number of classes equals 2. Both of them do the same operation: transform the logits (see below) to probabilities. In simple binary classification, there’s no big difference between the two, however in case of multinomial classification, sigmoid allows … Read more
Well, I hope I’m not too late for the party! Let me first try to establish some intuition before digging into loads of information (warning: this is not a brief comparison, TL;DR) Introduction A hypothesis h(x), takes an input and gives us the estimated output value. This hypothesis can be as simple as a one-variable … Read more
They’re both correct, but yours is preferred from the point of view of numerical stability. You start with e ^ (x – max(x)) / sum(e^(x – max(x)) By using the fact that a^(b – c) = (a^b)/(a^c) we have = e ^ x / (e ^ max(x) * sum(e ^ x / e ^ max(x))) … Read more