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| bio | website | pardisnoorzad.com |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 1 year, 6 months |
| seen | Oct 1 '12 at 0:37 | |
| stats | profile views | 143 |
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Aug 7 |
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logistic regression. How to get dual function? @user1149913 seems to give a good answer to that part |
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Aug 7 |
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logistic regression. How to get dual function? that's true, I was answering the related question that came up in the comments above; additionally, I thought that the OP's main question was how to solve the $\ell_2$-regularized problem not how to obtain the dual formulation |
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Jul 7 |
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Online reference for review of introductory statistics material I especially liked Introduction to Statistical Thought by Michael Lavine |
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Jul 7 |
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Online reference for review of introductory statistics material Good links here: r-statistics.com/2009/10/free-statistics-e-books-for-download |
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Jul 3 |
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What is the optimal $k$ for the $k$ nearest neighbour classifier on the Iris dataset? @image_doctor you also need to explain how you are going to estimate the generalization error... is it the error on a separate test set or with cross-validation? |
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Jul 1 |
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What is the relationship between regression and linear discriminant analysis? Here's another comparison of generative and discriminative classifiers by Yaroslav Bulatov on Quora: quora.com/… |
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Jun 29 |
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How to measure the performance of a regressor? note that regressor is another term for the predictor variable (see en.wikipedia.org/wiki/…) |
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Jun 29 |
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Dimension reduction technique I made a presentation on linear and nonlinear methods for dimensionality reduction once: pardisnoorzad.com/wp-content/uploads/slides/… |
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Jun 20 |
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How to implement this classification/labelling problem? I think you should include that in your question above. |
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Jun 20 |
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How to implement this classification/labelling problem? Once you identify the clusters, then you would already know which class each observation belongs to... unless there are less classes than clusters. Is this the case? |
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Jun 20 |
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How to implement this classification/labelling problem? how many observations do you have available? |
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Jun 11 |
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Generalization and recall Out of the three points you made, only (1) makes sense. A good classifier that generalizes well, will have high recall and high precision. And there is no such thing as a generalized classifier. |
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Jun 10 |
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Optimizing regression coefficients to predict the largest outcomes @rolando2 I think the comment above makes what I was asking clear. In robust regression, we don't consider outliers to be "conditions that do not produce the desired outcome" but observations we'll rather have removed in a pre-processing step. What I was asking was that in this setting, should the observations with small outcomes be considered outliers? |
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Jun 10 |
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Optimizing regression coefficients to predict the largest outcomes Here we're not after what causes the event, but the outcome of a similar rare event. In this setting, aren't the majority of observations considered 'outliers'? |
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Jun 8 |
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Linear regression when you only know $X^t Y$, not $Y$ directly @cardinal Very good point, thanks :) |
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Jun 8 |
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Linear regression when you only know $X^t Y$, not $Y$ directly @cardinal I see, but isn't the loss function for OLS the square loss? We see that the solution can be expressed in terms of $X^tY$, just like the solution of ridge regression. |
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Jun 8 |
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Linear regression when you only know $X^t Y$, not $Y$ directly I don't understand, can't you use cross-validation for estimating the optimal ridge parameter? |
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Jun 3 |
answered | Comprehensive overview of loss functions? |
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Jun 3 |
awarded | Critic |
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Jun 2 |
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How to rigorously define the likelihood? I didn't understand the part about the change of parameters and $\sqrt{\theta}$. Could you please explain? or point to a reference? |
