Comments for statalgo

Comment on ESL: The Elements of Statistical Learning by John Weatherwax

John Weatherwax — Sat, 21 Jan 2012 19:04:18 +0000

Hi,
At some point in the past I worked on such a project myself:

http://www.waxworksmath.com/Authors/G_M/Hastie/hastie.html

I've not looked at this in a while but at some point in the future I'll prob. revisit it. When I do it will be good to compare notes.

Keep up the good work,

John

Comment on Pandas, but not the furry kind... by Ellie K

Ellie K — Sun, 08 Jan 2012 13:40:15 +0000

Thank you so much for the helpful explanation and background about Pandas. I had no idea WHY there was so many references to pandas, Python and SciPy until now.

(I'm Ellie, I'm a not very serious statistician, I follow you on Twitter as EllieAsksWhy, I am not a bot or spam.)

Comment on Stanford ML 1.2: Gradient Descent by pedro

pedro — Sat, 03 Dec 2011 09:22:43 +0000

SO thanked, that`s a great help for me.

Comment on Stanford ML 5.1: Learning Theory and the Bias/Variance Trade-off by Stanford ML 5.2: Regularization – statalgo

Stanford ML 5.2: Regularization – statalgo — Thu, 17 Nov 2011 04:32:25 +0000

[...] considered the problem of overfitting as model complexity increase in the prior post. Now we look at one way to control for this problem: regularization. The basic idea is to penalize [...]

Comment on Stanford ML 4: Logistic Regression and Classification by Shane

Shane — Tue, 01 Nov 2011 01:09:47 +0000

Thanks! Made the correction. One of these days I'll test code before posting it...

Comment on Stanford ML 4: Logistic Regression and Classification by nicolas

nicolas — Mon, 31 Oct 2011 19:17:21 +0000

Hey,

Thank you for all that.
I wanted to do the machine learning class in R too, but I thought one has to venture in unfamiliar territory one at a time.

Now I might have the courage to try that
Thks

Comment on Stanford ML 4: Logistic Regression and Classification by Evan Sparks

Evan Sparks — Sun, 30 Oct 2011 20:47:46 +0000

Your first code block doesn't correctly run stepAIC - I'm assuming the code in the first block should be "stepAIC(glm(chd ~ ., data=sa.heart))" As someone who doesn't have time to devote to the whole class, I'm enjoying this series quite a bit, though!

Comment on Stanford ML: Code to Accompany the Lectures by Gustavo Guerra

Gustavo Guerra — Sun, 30 Oct 2011 17:55:55 +0000

Hi, I'm doing the MLClass exercises in F#. The code is at https://github.com/ovatsus/MLClass

Comment on ESL 1: Introduction (and the Scatterplot Matrix) by Stanford ML 4: Logistic Regression and Classification – statalgo

Stanford ML 4: Logistic Regression and Classification – statalgo — Fri, 28 Oct 2011 04:01:44 +0000

[...] As discussed in the past, assuming your dataset isn't too large, a scatterplot matrix is a really useful way to quickly look at data: [...]

Comment on Stanford ML 3: Multivariate Regression, Gradient Descent, and the Normal Equation by Stanford ML 4: Logistic Regression and Classification – statalgo

Stanford ML 4: Logistic Regression and Classification – statalgo — Fri, 28 Oct 2011 03:57:35 +0000

[...] The initial lectures in Stanford CS229a were concerned with regression problems where the predicted value was a continuous number. Another class of problems is concerned with discrete problems, where values are divided into groups (e.g. on or off; red, green, or blue). This builds on all the material from the previous linear regression lectures. [...]