Linked Questions

4
votes
4answers
1k views

What must someone know in statistics and machine learning? [closed]

There seem to be two different worlds in statistics. On one hand, there are the practitioners which run the same tests again and again. On the other hand, there is this overwhelming and seemingly ...
4
votes
2answers
8k views

Multivariate statistics vs machine learning?

Are multivariate statistics and machine learning solving the same problems? I saw that their books are about the same topics, so I have the impression that they are solving the same problems and ...
104
votes
11answers
47k views

When should linear regression be called “machine learning”?

In a recent colloquium, the speaker's abstract claimed they were using machine learning. During the talk, the only thing related to machine learning was that they perform linear regression on their ...
0
votes
1answer
87 views

GLMMs, stable isotope distribution analysis

I am currently working with a set of samples of stable isotopic concentrations obtained from a group of individuals. I am trying to process this data through a glmm() from the package lme4 to ...
2
votes
1answer
801 views

If regression is supervised learning is correlation unsupervised learning?

Regression is often given as a simple example for supervised learning because you have a dependent variable and try to build a model with the independent variables. Could you say that correlation is ...
2
votes
1answer
112 views

Are there good recent (i.e. year 2018) papers and discussions on the issue of “Statistics vs. Machine Learning”?

I try to get an overview about the most recent discussions of how machine learning and classical statistics differ. There is an excellent discussion on this issue here on stackexchange ( The Two ...
1
vote
2answers
355 views

When predictive analytics is better than statistics?

I am somehow confused by the term of so called "predictive analytics". It is so often used today that makes me think the old-school statisticians never before tried to create any predictive models. ...
1
vote
2answers
133 views

choice of mean for mean centering

I am doing statistical analysis of empirical data using a a generalized ordered regression model. I would like to test for interaction terms. I have a 3-level categorical IV (coded as 2 dummy ...
20
votes
6answers
16k views

What is the 'fundamental' idea of machine learning for estimating parameters?

The 'fundamental' idea of statistics for estimating parameters is maximum likelihood. I am wondering what is the corresponding idea in machine learning. Qn 1. Would it be fair to say that the '...
53
votes
2answers
30k views

Pandas / Statsmodel / Scikit-learn

Are Pandas, Statsmodels and Scikit-learn different implementations of machine learning/statistical operations, or are these complementary to one another? Which of these has the most comprehensive ...
5
votes
2answers
4k views

Why do Statistics, Machine learning and Operations research stand out as separate entities

It seems nowadays people who work in ${\bf Statistics, \ Machine\ Learning \ and \ Operations\ research }$ all consider themselves as working in data analytics. These three categories try to ...
3
votes
0answers
265 views

What is the difference between Inference and Machine Learning? [duplicate]

I have seen some classes in my University labeled as an "Inference class" and others as "Machine learning" classes, but I was not sure if appreciated the core difference between these two labelings? ...
190
votes
8answers
394k views

In linear regression, when is it appropriate to use the log of an independent variable instead of the actual values?

Am I looking for a better behaved distribution for the independent variable in question, or to reduce the effect of outliers, or something else?
18
votes
4answers
4k views

Does “curse of dimensionality” really exist in real data?

I understand what is "curse of dimensionality", and I have done some high dimensional optimization problems and know the challenge of the exponential possibilities. However, I doubt if the "curse of ...
4
votes
0answers
344 views

Which, if any, machine learning algorithms are accepted as being a good tradeoff between explainability and prediction? [closed]

Machine learning texts describing algorithms such as gradient boosting machines or neural networks often comment that these models are good at prediction, but this comes at the price of a loss of ...

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