lrnzcig
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You could use Mann-Withney U-test In statistics, the Mann–Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test (WRS), or Wilcoxon–Mann–Whitney test) is a ...

The reason could be that the categorical variable you are leaving out has too many levels compared the other variables. From The Elements of Statistical Learning: The partitioning algorithm tends ...

After comments, here you have some notes on how to do this in practice. Below I add a very simple example using igraph package in R. Personalized Page Rank (or Topic-Sensitive Page Rank), does ...

I am not quite sure I have fully understood how you have prepared the dataset, but anyway this what I think you could do. Take your 3000 samples with your 15 features, and add a new feature with ...

Yes you can define your problem as an optimization in which you maximise (or minimise) a cost function. You could define your cost function simply as \sum_{i} (valid_{i} == True) * PS_{i} - (valid_{...

Normalizing your data may help for faster convergence. Take a look to this paper: http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf In your case for faster convergence you should probably take ...

It is normal that your training accuracy goes down when the dataset size grows. Think of it this way: when you have fewer samples (imagine that you have just one, at the extreme) it is easy to fit a ...

I guess you are getting confused because you've build the perfect decision tree for the data, thus it does not have any misclassification error at all. However, the exercise is asking you to reflect "...

Reviewing your code, there's a couple things that you might consider trying. you are not setting the C values, thus sklearn will use a default value of C = 1. This will not necessarily mean that you ...