Frank Raulf
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  • Frankfurt
  • Germany
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Profile Information

Short Bio:
Favourite languages: R, Python.
Favourite topics: Simulation, regression methods, API, web-scraping.
Interested in: MLP, CNN, RNN
Job Title:
Senior Data Scientist
Job Function:
Data Science, Machine Learning, AI, Business Analytics, Deep Learning
LinkedIn Profile:
Contributing, Networking, New venture

Frank Raulf's Blog

Introduction to Gradient Decent

Posted on June 24, 2020 at 7:00am 0 Comments

The gradient decent approach is used in many algorithms to minimize loss functions. In this introduction we will see how exactly a gradient descent works. In addition, some special features will be pointed out. We will be guided by a practical example.…


How to make time-data cyclical for prediction?

Posted on January 26, 2020 at 4:00am 0 Comments

There are many ways to deal with time-data. Sometimes one can use it as time-series to take possible trends into account. Sometimes this is not possible because time can not be arranged in a sequence. For example, if there are just weekdays (1 to 7) in a dataset over several month. In this case one could use one-hot-encoding. However, considering minutes or seconds of a day one-hot-encoding might lead to high complexity. Another approach is to make time cyclical. This approach leads to a…


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…


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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