Efficiently exploring the parameter-search through Bayesian Optimization with skopt in Python. TL;DR: my hyperparameters are always better than yours. — In this post, we will build a machine learning pipeline using multiple optimizers and use the power of Bayesian Optimization to arrive at the most optimal configuration for all our parameters. All we need is the sklearn Pipeline and Skopt. You can use your favorite ML models, as long as they…

A simple, yet meaningful probabilistic Pyro model to uncover change-points over time. — One profound claim and observations by the media is, that the rate of suicides for younger people in the UK have risen from the 1980s to the 2000s. You might find it generally on the news , in publications or it is just an accepted truth by the population. …

Make the best of missing data the Bayesian way. Improve model performance and make comparative benchmarks using Monte Carlo methods. — The Ugly Data How do you handle missing data, gaps in your data-frames or noisy parameters? You have spent hours at work, in the lab or in the wild to generate or curate a dataset given an interesting research question or hypothesis. Terribly enough, you find that some of the measurements for a…

Build better Data Science workflows with probabilistic programming languages and counter the shortcomings of classical ML. — We should always aim to create better Data Science workflows. But in order to achieve that we should find out what is lacking. Classical ML workflows are missing something Classical Machine Learning is pipelines work great. The usual workflow looks like this: Have a use-case or research question with a potential hypothesis,

Modeling U.S. cancer-death rates with two Bayesian approaches: MCMC in STAN and SVI in Pyro. — Single parameter models are an excellent way to get started with the topic of probabilistic modeling. These models comprise of one parameter that influences our observation and which we can infer from the given data. …

The way from data novice to professional — There exists the idea that practicing something for over 10000 h (ten-thousand-hours) lets you acquire enough proficiency with the subject. The concept is based on the book Outliers by M. Gladwell. The mentioned 10k hours are how much time you spend practicing or studying a subject until you have a…

One reason why Bayesian Modeling works with real world data. The approximate light-house in the sea of randomness. — When you want to gain more insights into your data you rely on programming frameworks that allow you to interact with probabilities. All you have in the beginning is a collection of data-points. It is just a glimpse into the underlying distribution from which your data comes. However, you not…

The answer to: “Why is my model running forever?” or the classic: “I think it might have converged?” — The driver behind a lot of models that the average Data Scientist or ML-engineer uses daily relies on numerical optimization methods. Studying the optimization and performance of different functions helps to gain a better understanding of how the process works. The challenge we face on a daily basis is that someone…

Connect the dots over time and forecast with confidence(-intervals). — Have you ever wondered how to account for uncertainties in time-series forecasts? Have you ever thought there should be a way to generate data-points from previously seen data and make judgement calls about certainties? I know I have. If you want to build models that capture probabilities and hold confidences we recommend…