lrnzcig
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Method for a hypothesis testing non normal distribution number of retweets
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4 votes

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 ...

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Random Forest in R - most important variable causing errors
3 votes

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 ...

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Calculating Personalized PageRank in R
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3 votes

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 ...

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Splitting Stock Price data for SVM classification
2 votes

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 ...

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How to classify or model this problem?
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1 votes

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_{...

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What are some ways to improve performance of a neural network binary classifier?
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1 votes

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 ...

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Learning curves - Why does the training accuracy start so high, then suddenly drop?
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1 votes

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 ...

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What are misclassified instances in data and how to calculate it?
1 votes

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 "...

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100% training accuracy despite a low cv score
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1 votes

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 ...

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Validating Personalized Pagerank Matrix computation in R
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1 votes

You just have to normalize the results, keeping in mind that all Page Rank vectors add up to 1, both the "normal" and the "personalized" Page Ranks. vpr <- page.rank(g, vids=V(g), ...

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Should I use IN or OUT degree in Network Diffusion Model of Twitter network using iGraph?
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1 votes

If A is following B, then A has an OUT edge to B, that's right. However, I agree it is not intuitive, since in the case of twitter the propagation of information goes from B to A. It is ok, provided ...

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