Statistical Analysis Stack Exchange
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1500 questions
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11 answers

Why is generating 8 random bits uniform on (0, 255)?

I am generating 8 random bits (either a 0 or a 1) and concatenating them together to form an 8-bit number. A simple Python simulation yields a uniform distribution on the discrete set [0, 255]. I am trying to justify why this makes sense in my…
glassy
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What algorithms need feature scaling, beside from SVM?

I am working with many algorithms: RandomForest, DecisionTrees, NaiveBayes, SVM (kernel=linear and rbf), KNN, LDA and XGBoost. All of them were pretty fast except for SVM. That is when I got to know that it needs feature scaling to work faster. Then…
Aizzaac
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How to interpret root mean squared error (RMSE) vs standard deviation?

Let's say I have a model that gives me projected values. I calculate RMSE of those values. And then the standard deviation of the actual values. Does it make any sense to compare those two values (variances)? What I think is, if RMSE and…
jkim19
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What is the most accurate way of determining an object's color?

I have written a computer program that can detect coins in a static image (.jpeg, .png, etc.) using some standard techniques for computer vision (Gaussian Blur, thresholding, Hough-Transform etc.). Using the ratios of the coins picked up from a…
MoonKnight
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Dropping one of the columns when using one-hot encoding

My understanding is that in machine learning it can be a problem if your dataset has highly correlated features, as they effectively encode the same information. Recently someone pointed out that when you do one-hot encoding on a categorical…
dasboth
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Think like a bayesian, check like a frequentist: What does that mean?

I am looking at some lecture slides on a data science course which can be found here: https://github.com/cs109/2015/blob/master/Lectures/01-Introduction.pdf I, unfortunately, cannot see the video for this lecture and at one point on the slide, the…
Luca
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What are the dangers of violating the homoscedasticity assumption for linear regression?

As an example, consider the ChickWeight data set in R. The variance obviously grows over time, so if I use a simple linear regression like: m <- lm(weight ~ Time*Diet, data=ChickWeight) My questions: Which aspects of the model will be…
Dan M.
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Why do some people use -999 or -9999 to replace missing values?

I have a dataset. There are lots of missing values. For some columns, the missing value was replaced with -999, but other columns, the missing value was marked as 'NA'. Why would we use -999 to replace the missing value?
qqqwww
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Classification/evaluation metrics for highly imbalanced data

I deal with a fraud detection (credit-scoring-like) problem. As such there is a highly imbalanced relation between fraudulent and non-fraudulent observations. http://blog.revolutionanalytics.com/2016/03/com_class_eval_metrics_r.html provides a great…
Georg Heiler
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Interpretation of biplots in principal components analysis

I came across this nice tutorial: A Handbook of Statistical Analyses Using R. Chapter 13. Principal Component Analysis: The Olympic Heptathlon on how to do PCA in R language. I don't understand the interpretation of Figure 13.3: So I am plotting…
user862
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XGBoost Loss function Approximation With Taylor Expansion

As an example, take the objective function of the XGBoost model on the $t$'th iteration: $$\mathcal{L}^{(t)}=\sum_{i=1}^n\ell(y_i,\hat{y}_i^{(t-1)}+f_t(\mathbf{x}_i))+\Omega(f_t)$$ where $\ell$ is the loss function, $f_t$ is the $t$'th tree output…
Alex R.
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10-fold Cross-validation vs leave-one-out cross-validation

I'm doing nested cross-validation. I have read that leave-one-out cross-validation can be biased (don't remember why). Is it better to use 10-fold cross-validation or leave-one-out cross-validation apart from the longer runtime for leave-one-out…
machinery
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Is cosine similarity identical to l2-normalized euclidean distance?

Identical meaning, that it will produce identical results for a similarity ranking between a vector u and a set of vectors V. I have a vector space model which has distance measure (euclidean distance, cosine similarity) and normalization technique…
Arne
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What is the difference between "mean value" and "average"?

Wikipedia explains: For a data set, the mean is the sum of the values divided by the number of values. This definition however corresponds to what I call "average" (at least that's what I remember learning). Yet Wikipedia once more quotes: There…
neydroydrec
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Ensemble of different kinds of regressors using scikit-learn (or any other python framework)

I am trying to solve the regression task. I found out that 3 models are working nicely for different subsets of data: LassoLARS, SVR and Gradient Tree Boosting. I noticed that when I make predictions using all these 3 models and then make a table of…
Maksim Khaitovich
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