selectron

10 ms·

1.
by selectron 10y ago
It is a legitimate question.
2.
by selectron 10y ago
How much better?
3.
by selectron 10y ago
This is a great point about making sensible assumptions. Too often I see evidence that people think that data analysis should be devoid of assumptions, and any assumptions completely invalidate the analysis. In reality, almost all analysis
4.
by selectron 10y ago
Why should we expect there isn't a correlation? They do mention it in their analysis. At the end of the day, just because there might be a systematic bias in your result doesn't mean there is a systematic bias. All real-world anal
5.
by selectron 10y ago
It is always easy to criticize a data-driven analysis by saying its assumptions could be wrong. In the real world, all analysis is based on assumptions, some of which you can always claim might not be correct. But you have to really present
6.
by selectron 10y ago
The main thing I want from job descriptions is a salary range. The fact that companies don't post salaries is a strong counter-point to how companies complain about how hard it is to hire software engineers.
7.
by selectron 10y ago
The problem is that the term data analyst has come to mean data reporter. Similarly "business analyst" generally involves tasks that are best solved in Excel. The "science" in data science is about testing predictions. B
8.
by selectron 10y ago
You don't have to be productive all the time. It is important to have some time to relax and have fun. There are far worse things you could be doing than playing too many video games. You can try replacing video games with a more produ
9.
by selectron 10y ago
> Taking papers at face value is really only a problem in science reporting and at (very) sub-par institutions/venues. > WRT the former, science reporters often grossly misunderstand the paper anyways. All the good reproducible s
10.
by selectron 10y ago
The explanation glosses over a few important details. Gradient boosting works by adding some small weight to the instances the model is incorrectly predicting. The amount of extra weight these instances get is a parameter that is tuned wit
11.
by selectron 10y ago
Feature engineering and model ensembling are usually what separates the top competitors.
12.
by selectron 10y ago
Hand counting of votes seems like a no-brainer, regardless of whether there was a conspiracy this election.
13.
by selectron 10y ago
This statement is too general. You could of said the same thing about chess, there are chess Grandmasters who devote their lives to studying the game yet computers play chess at a much higher level than any human.
14.
by selectron 10y ago
I would say that table is really quite valuable. Kaggle problems come from all types of companies, so it doesn't make sense to say that it is "overfitted patterns that he's adopted in his own realm". With that said, vali
15.
by selectron 10y ago
For image competitions you are right. Neural networks are often in winning teams ensembles, but they require a lot more work than something like xgboost (gradient-boosted decision trees). For a dataset that isn't image processing or NL
16.
by selectron 10y ago
1) It depends heavily on the model. Something like xgboost (gradient boosted decision tree) will handle irrelevant features fairly well, while other models (like linear models, especially without lasso regularization) will have much more tr
17.
by selectron 10y ago
There is no way machine learning will be a necessary skill for software engineering, if that is your motivation I would not spend time learning it. However, if you still want to learn it you should first study statistics, for instance http
18.
by selectron 10y ago
My advice is Python, but it depends on what your background is and what you want to do. If this is your first language and you have a stats background, R is a solid choice. If you already know another language, R has a lot of flaws that are
19.
by selectron 10y ago
Interesting. After watching the show Billions, and reading up on how much money hedge fund managers make on fees (seems totally ridiculous), I wonder how common is illegal insider trading for hedge funds? No matter how good your model is, y
20.
by selectron 10y ago
To really understand if companies are biased or not, you also need to know the percent of applicants to these companies who are black. If only 2% of applicants to Google are black, I would expect only 2% of new hires at Google to be black.
21.
by selectron 10y ago
I agree completely. I also think there is way more luck involved than people want to admit - a lot interesting results are unexpected, and there are so few jobs the timing has to work out for you. I have known plenty of great post docs who
22.
by selectron 10y ago
Sorry I wasn't clear - the attitude of going into industry being seen as a failure is common, especially among older professors. This attitude is changing somewhat, but is still definitely there.
23.
by selectron 10y ago
If you just want ROI, you are better off spending your money elsewhere. This is evidenced by the lack of money most companies put into scientific research. Further the gains of science are in general hard to profit off of. ( http:/&#x2
24.
by selectron 10y ago
The problem is that there are plenty of graduate students willing to work for peanuts.
25.
by selectron 10y ago
The goal of research (at least for basic science) isn't to make money, it is to increase knowledge about the universe. This knowledge is a public good, so it makes sense that private industries motivated by profit do not support fundam
26.
by selectron 10y ago
I can confirm that this idea is pervasive in physics (both experimental and theoretical)
27.
by selectron 10y ago
That has to be one of the worst abstracts I've ever read. I have no idea whether or not black people tip less having read the abstract.
28.
by selectron 10y ago
The some stereotypes are based in reality comment was meant to indicate that the fact is there are real differences between the average behaviors of different groups of people. So when shown clear evidence that groups behave differently, it
29.
by selectron 10y ago
> Why would there be a reason black people would give cab drivers a harder time than other skin colors? There isn't any good reason. This is clearly happening, based on the anecdote of cab drivers avoiding picking up black people as
30.
by selectron 10y ago
> Being told by cab drivers that they’re the first Black person they’ve ever had a positive encounter with This was the most surprising to me in his list of things he has grown accustomed to.

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