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