Studied Mathematics, graduated in Cryptanalysis, working as a Data Scientist. Interested in algorithms, probability theory, and machine learning. Python user.

# A short Story

The three friends Frequentist Frank, Stubborn Stu, and Bayesian Betty go to a funfair where a mysterious-looking tent catches their eyes. Inside, they meet Claire Voyant who claims to be a… fortune teller. The friends don’t believe her, of course — they need proof. So they conduct a little experiment:

# Bayesian Marketing Mix Modeling in Python via PyMC3

## Estimate the saturation, carryover, and other parameters all at once, including their uncertainty

In this article, I want to combine two concepts that I discussed in earlier posts: Bayesian modeling and marketing mix modeling. Since the chances are high that you are not familiar with both of these topics, let me give you a quick introduction and further readings. I will

1. motivate what…

# Interpretable Neural Networks With PyTorch

## Learn how to build feedforward neural networks that are interpretable by design using PyTorch

There are several approaches to rate machine learning models, two of them being accuracy and interpretability. A model with high accuracy is what we usually call a good model, it learned the relationship between the inputs X and outputs y well.

If a model has high interpretability or explainability, we…

# Rockin‘ Rolling Regression in Python via PyMC3

## Learn how to deal with varying parameters

Assume that you want to train a parametric model such as a linear one or a neural network. In the case of linear regression, first, you specify the shape of the model, let us say y = ax + b. Second, you estimate the parameters a and b. …

# Flashback

In my last article, I introduced you to the world of marketing mix modeling. If you have not read it so far, please do before you proceed.

There, we have a created a linear regression model that is able to predict sales based on raw advertising spends in several advertising…

# Introduction to Marketing Mix Modeling in Python

To keep a business running, spending money on advertising is crucial — this is the case regardless of whether the company is small or already established. And the number of ad spendings in the industry are enormous:

# A Tale of Two Approaches

From time to time, you have the agony of choice when trying to do machine learning on a quite heterogeneous dataset. As an example of what I mean with a heterogeneous dataset, consider a simple height of people dataset.

# Staying Competitive with Linear Models

## With the right features, a linear model can become a beast

As a data scientist, you have a lot of different types of shiny machine learning models to choose from. There are neural network models, gradient boosting models, bagging models, just to name a few main categories.

In the shadows of these fellas, there also exist models like linear and logistic…

# Introduction

Machine learning is often all about the following question:

Given a dataset (X, y), where X is a feature matrix and y is a target vector, find an f with f(X) ≈ y.

We usually do not enforce a strict equality à la f(X) = y because there are errors…

# Motivation

Imagine that, for whatever reason, you want to do a diet consisting of apples and strawberries only. You don’t really favor one fruit over the other, but you want to make sure that you…

1. get enough vitamin C and
2. get enough calories.

In addition to having a different amount of…

## Dr. Robert Kübler

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