statistics

来源： https://stackoverflow.com/questions/62649132/conditional-mutual-information-with-multiple-variables

来源： https://stackoverflow.com/questions/57527535/how-to-add-multiple-random-effects-python-statsmodel

来源： https://stackoverflow.com/questions/57527535/how-to-add-multiple-random-effects-python-statsmodel

来源： https://stackoverflow.com/questions/29318558/polynomial-feature-expansion-in-r

来源： https://stackoverflow.com/questions/29318558/polynomial-feature-expansion-in-r

问题 I have sample with size = n. I want to calculate for each i: 1 <= i <= n median for sample[:i] in numpy. For example, I counted mean for each i: cummean = np.cumsum(sample) / np.arange(1, n + 1) Can I do something similar for the median without cycles and comprehension? 回答1: Here's an approach that replicates elements along rows to give us a 2D array. Then, we would fill the upper triangular region with a big number so that later on when we sort the array along each row, would basically sort

问题 I have the fisher's linear discriminant that i need to use it to reduce my examples A and B that are high dimensional matrices to simply 2D, that is exactly like LDA, each example has classes A and B, therefore if i was to have a third example they also have classes A and B, fourth, fifth and n examples would always have classes A and B, therefore i would like to separate them in a simple use of fisher's linear discriminant. Im pretty much new to machine learning, so i dont know how to

问题 I have the fisher's linear discriminant that i need to use it to reduce my examples A and B that are high dimensional matrices to simply 2D, that is exactly like LDA, each example has classes A and B, therefore if i was to have a third example they also have classes A and B, fourth, fifth and n examples would always have classes A and B, therefore i would like to separate them in a simple use of fisher's linear discriminant. Im pretty much new to machine learning, so i dont know how to

问题 I know that the meaning of recall in search engine, but what's the meaning of recall of a classifier, e.g. bayes classifier? please give a an example, thanks. for example, the Precision = correct/correct+wrong docs for test data. how to understand recall? 回答1: Recall literally is how many of the true positives were recalled (found) , i.e. how many of the correct hits were also found. Precision (your formula is incorrect) is how many of the returned hits were true positive i.e. how many of the

问题 I know that the meaning of recall in search engine, but what's the meaning of recall of a classifier, e.g. bayes classifier? please give a an example, thanks. for example, the Precision = correct/correct+wrong docs for test data. how to understand recall? 回答1: Recall literally is how many of the true positives were recalled (found) , i.e. how many of the correct hits were also found. Precision (your formula is incorrect) is how many of the returned hits were true positive i.e. how many of the