Karolis Koncevičius
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If I understood the question correctly - you might want to use a "conditional density plot". Such a plot provides a smoothed overview of how a categorical variable changes across various levels of ...

In my view the question about scaling/not scaling the features in machine learning is a statement about the measurement units of your features. And it is related to the prior knowledge you have about ...

This is just terminology, no need to think about it too much as different people classify different areas into different categories. For example a lot of statisticians would consider machine learning ...

Interpretation of the Mean When we say that the average value spent on meals was 50 USD - it means that if we take the total amount spent on fast foods and equally divide the sum among all the people ...

Probably Quadratic Discriminant Analysis. There are also names for different constraints you could make: Covariance matrices of both classes are equal - Linear Discriminant Analysis. Only diagonal ...

In publications honesty is the best policy. It's all right to remove outliers and to report descriptive statistics based on the reduced dataset. However in order for others to understand and ...

In simplified terms you can think about it like this: suppose you are preparing pupils for an exam. You have three sets exercises from previous exams: A, B, and C. But the exercises in the upcoming ...

In my opinion one of more honest yet non-technical phrasing would be something like: The obtained result is surprising/unexpected (p = 0.03) under the assumption of no mean difference between the ...

@NickCox already provided an answer in the comments, I will only elaborate on it step-by-step and try to provide some intuition. You have a range on numbers ranging from $x$ to $y$ and you want to ...

In my opinion the best course of action, if possible, is to collect more data and then use that data to check your current model as well as maybe top 5 of the previous models you tried. Continuing ...

I think it's best understood with a simple example. Imagine you are on a farm that raises sheep. The farm has a lot of sheep but you only observe 5. Out of those 5 sheep, 1 sheep was black and 4 sheep ...

Prof. Yaser Abu-Mostafa talks briefly about this in his Caltech course on machine learning during the first lecture. He identifies 3 essential points you have to consider before considering applying ...

Looks like your sample size is not a lot bigger than the dimensionality of the data (feature set size). That can be a problem for LDA and it can overfit. Since it relies on computing the within-class ...

SVM also suffers the problems coming from high dimensionality, but under typical settings to a lesser degree compared to (say) LDA. I can imagine SVM would only have to take dot products of support ...

This will probably not be the answer you want or expect, but this is how I see these things. Clustering problem Clustering, to a degree, is almost always a subjective procedure. You decide how you ...

R has comprehensive documentation. For this specific case help(anova.lm) says: Details: Specifying a single object gives a sequential analysis of variance table for that fit. That is, ...

Strictly speaking - there is no proper way to do this. It depends on your problem and more specifically - what you know about your dataset and what you are trying to achieve. If, for example, in your ...

What is the difference between constructing a model vs hypothesis testing? Like @mkt mentioned - both of these involve models in one for or another so that's a false distinction between them. ...

This can be done using the Kabsch Algorithm. The algorithm finds the best least-squares estimate for rotation of $RX-Y$ where $R$ is rotation matrix, $X$ and $Y$ are your target and source matrices ...

1. Citizenship example This seems to be a poor but valid test. It makes sense with an extreme p-value cut off $\alpha = 0$. This way we would only reject those citizens whose profession is not found ...

Adding my guess as an answer. When splitting the data into train/test sets you do a 0.8/0.2 split. Which, with the sample size of 500, should be 400/100. Then you train kNN on 400 and test the ...

Anything can be seen as a "metric", and both groups, statisticians and machine-learners, use plenty of those: accuracy, mean value, estimated parameter of a model, etc. Hypothesis testing is ...

If you have to report all the details then you should also report the actual t-value, not just degrees of freedom. About the degrees of freedom: your degrees of freedom changes because you are using ...

The question is a bit unclear, but here is an attempt at an answer based on my understanding of the question: First thing that might hinder some understanding is the (in my opinion) false distinction ...

Im am not 100% sure if this is a formal requirement but typically the null hypothesis and alternative hypothesis are: 1) complementary and 2) exhaustive. That is: 1) they cannot be both true at the ...

This at most will be a partial answer (or no answer at all). First thing to note is that I agree with @dsaxton completely: all models "discriminate" (at least in some definitions of discrimination) ...

Because your estimated mean might differ from the true mean in either direction: it can be too high or it can be too low. If you estimate the mean from your sample to be (e.g.) 3 and you also know ...

If the goal is to obtain 7+ eyes and there is no preference about the total score otherwise - then there is no difference between rolling 3 vs rolling 2 and re-rolling the lower one when their sum is ...