One of the simplest options to get a feeling for the “influence” of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data. Consider this example: import numpy as np from sklearn.linear_model import … Read more
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))
To start with, saying that an AUC of 0.583 is “lower” than a score* of 0.867 is exactly like comparing apples with oranges. [* I assume your score is mean accuracy, but this is not critical for this discussion – it could be anything else in principle] According to my experience at least, most ML … 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
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