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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
Seniority:
Technical
Job Function:
Data Science, Machine Learning, AI, Business Analytics, Deep Learning
Industry:
IT
LinkedIn Profile:
http://https://www.kaggle.com/frankmollard
Interests:
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.…

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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…

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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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