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Jun
9
awarded  Enlightened
Jun
9
comment Can a -2 Log likelihood be calculated with only one model?
@SibbsGambling In this link there is an example with coolibah tree data showing a "full" or "saturated" model where the log-likelihood is not zero. I believe there are certain situations where the saturated model must have a likelihood of one by definition, but not in all situations.
Jun
9
awarded  Nice Answer
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?