Linked Questions

5
votes
4answers
3k views

Machine Learning VS Statistical Learning vs Statistics [duplicate]

I have seen posts about the difference between ML and Statistics. And I have also seen posts explaining that Statistical Learning is a statistical approach to ML. But then, this is confusing because ...
8
votes
4answers
1k views

What is the definition of machine learning (vs classical statistics), and can methods such as MCMC and bootstrapping be considered ML? [duplicate]

I'm in the process of writing about the differences between machine learning and classical statistics. I've been looking for some authoritative sources that would give a good, clear, plain-English ...
3
votes
0answers
264 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? ...
0
votes
1answer
28 views

statistical regression vs machine learning regression [duplicate]

I was trying to understand the difference between statistical regression VS machine learning regression. My background is from Economics and learned regression from statistical point of view for the ...
1
vote
0answers
44 views

Is Statistics really better at making Relationship Inferences compared to Machine Learning? [duplicate]

First of all: I am a non-statistician who's currently trying to dive deeper into the topic of Data Science, so this question might sound really naive. So, I was wondering about the major distinction ...
218
votes
13answers
200k views

What is the difference between data mining, statistics, machine learning and AI?

What is the difference between data mining, statistics, machine learning and AI? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different ...
191
votes
14answers
19k views

What is a data scientist?

Having recently graduated from my PhD program in statistics, I had for the last couple of months began searching for work in the field of statistics. Almost every company I considered had a job ...
188
votes
8answers
388k 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?
104
votes
11answers
46k 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 ...
72
votes
16answers
36k views

Practical thoughts on explanatory vs. predictive modeling

Back in April, I attended a talk at the UMD Math Department Statistics group seminar series called "To Explain or To Predict?". The talk was given by Prof. Galit Shmueli who teaches at UMD's Smith ...
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 ...
21
votes
6answers
16k views

What is the difference between data mining and statistical analysis?

What is the difference between data mining and statistical analysis? For some background, my statistical education has been, I think, rather traditional. A specific question is posited, research is ...
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 '...
20
votes
4answers
10k views

What is the difference between learning and inference?

Machine learning research papers often treat learning and inference as two separate tasks, but it is not quite clear to me what the distinction is. In this book for example they use Bayesian ...
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
4answers
989 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 ...
6
votes
5answers
10k views

Skills & coursework needed to be a data analyst

I am graduating with a bachelor's degree in applied math and I will pursue a master's degree in statistics this fall. There are many specialized fields in applied statistics. I realize I may be more ...
25
votes
4answers
526 views

Do you have a global vision on those analysis techniques?

I'm currently on a project where I basically need, like we all do, to understand how output $y$ is related to input $x$. The particularity here is that data $(y,x)$ is given to me one piece at a time, ...
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 ...
9
votes
1answer
2k views

What are the practical & interpretation differences between alternatives and logistic regression?

A recent question about alternatives to logistic regression in R yielded a variety of answers including randomForest, gbm, rpart, bayesglm, and generalized additive models. What are the practical and ...
4
votes
2answers
2k views

Comparison of machine learning algorithms

Suppose that I have taken 8 machine learning algorithms which are used by researchers most frequently. I have applied these 8 machine learning algorithms over 8 datasets which are publicly available ...
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 ...
3
votes
2answers
513 views

Are there any power calculation formulas for ML methods beyond Logistic Regression? [closed]

Are there any power calculation formulas for ML methods (for binary classification) beyond Logistic Regression? (also well beyond the laughable rule of thumb 10 instances per variable) I've done a ...
1
vote
2answers
347 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. ...
0
votes
2answers
637 views

How important is p-value in Machine Learning?

Scikit-Learn doesn't exhibit the p-values for your models. I'm used to look at the p-values - besides a few other factors - when choosing the variables to consider on my final model. However, p-values ...
0
votes
4answers
501 views

Kernel Selection [closed]

First, I want to know how to analyze a dataset to discover its pattern. And second, how can I select the best kernel function for classifying a dataset?
2
votes
2answers
355 views

What types of data analysis do not count as statistics?

When does data analysis cease to be statistics ? Are the following examples all applications of statistics ?: computer vision, face recognition, compressed sensing, lossy data compression, signal ...
2
votes
1answer
785 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 ...
15
votes
0answers
841 views

Practical thoughts on explanatory vs predictive modeling [duplicate]

Possible Duplicate: Practical thoughts on explanatory vs. predictive modeling This question has been bugging me for some time, and I was going to write a blog post about it. However, I think it ...
4
votes
0answers
343 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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