Ryan
  • Member for 5 years, 11 months
  • Last seen more than a month ago
Are there any graph embedding algorithms like this already?
0 votes

So I think that it is important to realize that pagerank utilizes the eigenvalues of the nodes to speed up computation. The thing is that turns out to be equivalent to a random walk. Based on what you ...

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Can you provide examples of business application of vector autoregressive model?
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1 votes

Caveat: I have a doctorate in economics and that is why I knew how, and where, and when to apply this type of model. Sure, I used a vecm model last year to figure out how many credit cards get ...

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Determine useful features for machine learning model
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2 votes

I suggest taking a look at this page for some more ideas: Feature Selection That being said a couple of ideas that come to mind quickly, is to: use a tree based method (like Random Forest) and look ...

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Checking for stationarity in LSTM
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10 votes

In principle no you do not need to check for stationarity nor correct for it when you are using an LSTM. The thing about stationarity is that it makes prediction tasks much more efficient, and stable....

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Why do we need the hyperparameters beta and alpha in LDA?
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1 votes

LDA is a bayesian model. The equation that you gave is the posterior distribution of the model. The alpha and beta parameters come from the fact that the dirichlet distribution, (a generalization of ...

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twitter data analysis?
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3 votes

I believe that the algorithm that you want to use is something called a latent dirichlet allocation (LDA) model. This model is designed to uncover the topics in a corpus of documents. Scikit learn ...

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Assigning a value to Y for regression
0 votes

it's a self made index, totally arbitrary Therefore how it should increase is totally arbitrary too. I think the best answer to your question is that you need to think about the behavior you want to ...

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What's cooking Kaggle - Improve model
2 votes

But the problem of this dataset is that we have unbalanced data I think that the way to fix your problem is to use something like SMOTE or one of its variants. (My favorite is SMOTE-ENN, but go with ...

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Time-based over-sampling dilemma
1 votes

If you have time series for good clients and time series for bad clients, I would suggest using something along the lines of dynamic time warping to get out of a time domain, and into a dissimilarity ...

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Detecting outlier with combining two vectors
1 votes

Autoencoder Solution You could try an autoencoder. The autoencoder would take an input vector and it would try to recreate it as an output. So you take your input, and measure the distance between ...

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ML models: average of all versus average of averages?
1 votes

It shouldn't matter if you average all of them or take the average of the average for each person. It turns out that it will give you the same number. This is known as the law of total expectation. ...

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Elastic Regression fitting good mean and bad variance
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1 votes

Your model actually looks pretty good. What it sounds like you are asking to do is to overfit your model. I would not recommend that you do that. You can do that by finding more variables that you can ...

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Predicting Age of Birth
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4 votes

It seems like you have a classical bayesian problem. You have some sort of prior distribution, a distribution over years of birth, your prior distribution is bimodal with peaks at the two years, you ...

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Pricing decisions using neural network
1 votes

Yes you can use a neural network, or any other regression-like algorithm for this task. There are lots of algorithms that you can use. A simple feed-forward network should suffice. Although, if you ...

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Retail Store Testing
0 votes

I think your confusion is that you only have two stores, but you might have more than 2 data points being generated from those two stores. For example, you might be interested in how much individual ...

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How to group identical values and count their frequency in Python?
3 votes

I'm putting together some tutorials around data wrangling. Maybe my jupyter notebook on github will help. I think that it is the key is modifying the line: df.groupby('male')['age'].mean() to be: ...

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Use of cross validation for Polynomial Regression
2 votes

I think that you want this: K-fold If you want say MSE of each check out section 3.1.1 here: cross validated metrics

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Sentiment Analysis of Movie Reviews using Python
1 votes

I'm assuming that you are using bag of words, you can try adding bigrams and/or trigrams (or really any other arbitrary n-grams) to your vocabulary. I have also had a lot of success using latent ...

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Conditional logit in Stata
1 votes

I believe that the command you are looking for is outreg2. For other output options check out this page.

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Can I conduct independent t-test when data is infested with outliers ? and how to interpret the t-statistics?
0 votes

1) Maybe, remember that you are assuming a normal distributions, if you don't satisfy those assumptions you are not running a valid test. 2)You are testing whether or not the difference is zero, i.e. ...

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ARIMAX v. ARX Time Series Modeling
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2 votes

Yes, there are characteristics. You can use a correlogram to inform you as to the error structure in your data. That will tell you whether or not your data needs to account for AR and/or MA terms. ...

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Analyze performance Poisson regression model on a time series(count forecasting)
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2 votes

I'm not sure what you mean by "performance", but if what you mean is fit the answer is clear. You need to be using the log-likelihood to differentiate between different models. Basically, when you are ...

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