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 Yearling
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Apr
29
comment Having a job in data-mining without a PhD
This is of course a generalization. Many Ph.D. level machine learning scientists are also quite gifted programmers and are happy to think in business-focused pragmatic lines. But on average, the extra time spent not interfacing algorithms with real world software-based needs does not necessarily confer any advantage over the equally-as-theoretically-capable masters-level graduates, and can certainly confer a disadvantage for folks whose over emphasis of theory has left them without a good, pragmatic software development skill set.
Apr
29
comment Having a job in data-mining without a PhD
In addition, this sort of expertise is not generally applicable in many applied settings, since it's often the case that organizations and corporations strongly prefer to use pre-existing algorithms and open-source implementations to enable faster prototyping and to avoid sinking time into in-house research that is too speculative to be a good use of money or time. I find that masters students, with a more pragmatic focus on gaining sufficient, but not excess, theoretical appreciation, tend to do better with this and are a bit more well-rounded in the software development that comes with it.
Apr
29
comment Having a job in data-mining without a PhD
@JackTwain I disagree. I know many masters-level machine learning scientists who stay very current on the professional literature, attend CVPR, NIPS, ICML, and other annual conferences, and have every bit the same (or even better) theoretical depth as any of the Ph.D. machine learning graduates that I know, whether talking about modern criticisms of class label noise for boosting classifiers, or PAC-learning work on the complexity of learning, many masters students spend a lot of time reading these things, both during school and after graduating.
Feb
2
awarded  Yearling
Jan
31
comment Choosing right set of variables for Logistic regression and decision tree
Have you considered choosing the variables x and y? I've seen a lot of people reporting good results for their regressions when they've chosen x and y.
Sep
24
awarded  Autobiographer
Jul
9
comment Aside from regression coefficients, what are commonly used approaches to measure one variable's “sensitivity” to another variable?
Last thought: I did show them the Achen paper that I linked above, which provides pretty comprehensive examples of how the boiler-plate regression approach can go wrong. They simultaneously acknowledged that it could go wrong and that there was not much theoretical reason why it should even work correctly in the first place ... and at the same time essentially said they did not care because they wanted something expressed as regression coefficients regardless of the ramifications of that sort of model. And these were highly educated veterans running a long-standing, successful company. :/
Jul
9
comment Aside from regression coefficients, what are commonly used approaches to measure one variable's “sensitivity” to another variable?
I'm asking here to get feedback on the general problem. Such as, in machine learning sensitivity is measured via such-and-such, but in a classical Frequentist setting it is measured with blah-blah and in a Bayesian setting it's measured via blah... hopefully with references. Also, this is issue is quite old now and I have moved on from the position where the original project happened. I'm still very interested in this issue though ... just not connected to a specific problem instance anymore.
Jul
2
awarded  Curious
Jun
16
revised Computing directly comparable wavelet features on variable-length training examples
deleted 4 characters in body
Jun
15
asked Computing directly comparable wavelet features on variable-length training examples
Feb
21
comment Optimization of the regularized least squares with gradient descent
You mean the analytic solution for ridge (Tikhonov regularized) regression? That seems to be what the question is looking for... not "usual least-squares" which is generally used to mean ordinary least squares.
Feb
19
answered what does “a distribution over distributions” mean?
Feb
2
awarded  Yearling
Feb
1
awarded  Tumbleweed
Jan
25
revised Aside from regression coefficients, what are commonly used approaches to measure one variable's “sensitivity” to another variable?
edited title
Jan
25
asked Aside from regression coefficients, what are commonly used approaches to measure one variable's “sensitivity” to another variable?
Dec
17
comment How do you “control” for a factor/variable?
Just to emphasize the point on this question (which is re-asked very often), it's good to consider that simply including a variable in a model is not guaranteed to "control" for its effect, even under extremely strong assumptions about the variable being monotonically related to the dependent variable. See the article that is linked in my other comment.
Dec
12
comment How to assess statistical significance of the accuracy of a classifier?
And some application domains, say financial markets, where you get to use the classifier in many many roughly independent cases, just being a bit better than chance (R-squared's of like 11% or 12% are considered great) can mean a lot. In those cases, if even the boosted classifier has R-squared of 15% that might be considered very good -- in which case it really matters if you can statistically resolve whether the weak classifiers are definitely better than guessing.
Dec
12
comment How to assess statistical significance of the accuracy of a classifier?
Not if you are boosting a bunch of weak classifiers, which is a very common activity. You may care about discrimination once you reach the fully boosted final classifier, but there's a lot of work between the start and the finish, and demonstrating that a complicated classifier empirically performs better than chance is important.