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331.
▲
Timings of a Grouped Rank Filter Task
(win-vector.com)
1 point
jmount
8 years ago
discuss
332.
▲
Announcing Practical Data Science with R, 2nd Edition
(win-vector.com)
1 point
jmount
8 years ago
discuss
333.
▲
Automating data science steps: join dependency sorting
(win-vector.com)
1 point
jmount
9 years ago
discuss
334.
▲
Vtreat: a set of procedures for preparing data
(win-vector.com)
1 point
jmount
9 years ago
discuss
335.
▲
Upgrading to macOS Sierra (nee OS X) for R users
(win-vector.com)
1 point
jmount
9 years ago
discuss
336.
▲
A Theory of Nested Cross Simulation
(win-vector.com)
1 point
jmount
9 years ago
discuss
337.
▲
Data Cleaning and Preparation, Long Form and Tl;dr Form
(win-vector.com)
1 point
jmount
9 years ago
discuss
338.
▲
Laplace noising versus simulated out of sample methods (cross frames)
(win-vector.com)
1 point
jmount
10 years ago
discuss
339.
▲
Deming, Wald and Boyd: cutting through the fog of analytics
(win-vector.com)
1 point
jmount
16 years ago
discuss
340.
▲
Some of the history and purpose of "Hello World"
(win-vector.com)
1 point
jmount
16 years ago
discuss
341.
▲
On calculating AUC
(win-vector.com)
1 point
jmount
10 years ago
discuss
342.
▲
R programming annoyances
(win-vector.com)
1 point
jmount
16 years ago
discuss
343.
▲
Principal Components Regression, Pt.1: The Standard Method
(win-vector.com)
1 point
jmount
10 years ago
discuss
344.
▲
On Nested Models (and the problem with inappropriate re-used of data)
(win-vector.com)
1 point
jmount
10 years ago
discuss
345.
▲
Take a look at the leftpad code
(win-vector.com)
1 point
jmount
10 years ago
discuss
346.
▲
Sample(): “Monkey’s Paw” style programming in R
(win-vector.com)
1 point
jmount
10 years ago
discuss
347.
▲
Preparing data: free eBook and slidecast
(win-vector.com)
1 point
jmount
10 years ago
discuss
348.
▲
Finding the K in K-means by Parametric Bootstrap
(win-vector.com)
1 point
jmount
10 years ago
discuss
349.
▲
Write the Y combinator in R
(win-vector.com)
1 point
jmount
10 years ago
discuss
350.
▲
More efficient machine learning training through differential privacy
(win-vector.com)
1 point
jmount
11 years ago
discuss
351.
▲
Think you know what relative returns are?
(win-vector.com)
1 point
jmount
16 years ago
discuss
352.
▲
Use differential privacy to simulate having more modeling data
(win-vector.com)
1 point
jmount
11 years ago
discuss
353.
▲
A Simpler Explanation of Differential Privacy
(win-vector.com)
1 point
jmount
11 years ago
discuss
354.
▲
Is your model going to work? Part 3: Out of sample procedures
(win-vector.com)
1 point
jmount
11 years ago
discuss
355.
▲
Bootstrap evaluation of clusters
(win-vector.com)
1 point
jmount
11 years ago
discuss
356.
▲
How do you know if your model is going to work? Part 1
(win-vector.com)
1 point
jmount
11 years ago
discuss