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Frank Raulf liked Capri Granville's blog post What are the Typical Data Scientist Profiles on LinkedIn? Survey Results
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Lito P. Cruz commented on Frank Raulf's blog post Which one is faster in multiprocessing, R or Python?
"Good job. Thanks for the research."
Dec 21, 2019
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Frank Raulf posted blog posts
Dec 20, 2019
Frank Raulf liked Frank Raulf's blog post Which one is faster in multiprocessing, R or Python?
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Frank Raulf's blog post was featured

Which one is faster in multiprocessing, R or Python?

This post is the third one of a series regarding loops in R an Python.The first one was Different kinds of loops in R. The recommendation is to use different kinds of loops depending on complexity and size of iterations.The second post was…See More
Dec 19, 2019
Frank Raulf was featured
Dec 19, 2019
Bill Luker Jr liked Frank Raulf's blog post Omitted Variables in Linear Regressions
Dec 9, 2019

Profile Information

Field of Expertise
Data Science, Machine Learning, AI, Business Analytics, Deep Learning
Professional Status
Technical
Years of Experience:
7
Your Company:
Andersch digital
Industry:
Consulting
Your Job Title:
Senior Data Scientist
How did you find out about DataScienceCentral?
LinkedIn
Interests:
Contributing, Networking, New venture

Frank Raulf's Blog

Setting the Cutoff Criterion for Probabilistic Models

Posted on January 4, 2020 at 3:00am 0 Comments

For decision making, human perception tends to arrange probabilities into above 50% and below - which is plausible. For most probabilistic models in contrast, this is not the case at all. Frequently, resulting probabilities are neither normal distributed between zero and one with a mean of 0.5 nor correct in terms of absolute values. This is not seldom an issue accompanied with the existence of a minority class - in the underlying dataset.

For example, if the result of a…

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Naive Bayes Classifier using Kernel Density Estimation (with example)

Posted on January 3, 2020 at 4:30am 0 Comments

Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities of an event:

p(d)   (1)

By observing new data x, the statistician will adjust his opinions to get the "a posteriori" probabilities.

p(d|x)   (2)

The conditional probability of an event d given x is the share of  the joint…

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Which one is faster in multiprocessing, R or Python?

Posted on December 19, 2019 at 9:00am 1 Comment

This post is the third one of a series regarding loops in R an Python.

The first one was Different kinds of loops in R. The recommendation…

Continue

Omitted Variables in Linear Regressions

Posted on November 13, 2019 at 2:00am 0 Comments

The importance of completeness of linear regressions is an often-discussed issue. By leaving out relevant variables the coefficients might be inconsistent.

But why on earth?! 

Assuming a linear complete model of the form:

z = a + bx + cy + ε.

Where z is supposed to be dependent, x and y are independent and ε is the error term.

Now we drop y to check…

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