Browsed by
Category: Data Science

Why is Data Science still hard?

Why is Data Science still hard?

Data Science and Machine Learning have evolved a lot in the past 7 years that I’ve been practically involved in the domain. ML, with the help of the newest Deep Learning methods, has made huge advancements and problems that were considered untouchable 5 years ago, have now been solved. The good performance of ML on…

Read More Read More

Building an F1 prediction engine – Predictive Modelling Part II

Building an F1 prediction engine – Predictive Modelling Part II

This post will describe and explain maybe the most critical part of predictive modelling: how to correctly estimate the performance of a machine learning model. This is performed by setting up a trusted cross-validation framework. It’s crucial to get this right, otherwise your model performance estimates will not reflect the true model performance.

Formula One 2 Vec

Formula One 2 Vec

You know what is the most geeky way to ‘prove’ that Alonso is the best F1 driver ever? It is Neural Embeddings! In this post I will try to give an intuitive explanation of what neural embeddings are, how they can be calculated and show some examples of how they capture semantic information about the…

Read More Read More

Building an F1 prediction engine – Predictive Modelling Part I

Building an F1 prediction engine – Predictive Modelling Part I

In the previous series of posts I discussed and explained the steps involved in Feature Engineering. In this series, I will talk about the coolest part of applied ML; the Predictive Modelling phase. This is where you get to use all the ‘magic’ power of machine learning algorithms and see the performance of any models…

Read More Read More

Building an F1 prediction engine – Feature Engineering Part V

Building an F1 prediction engine – Feature Engineering Part V

Up till now we have discussed on how we can create some features and how we can encode them. We’ve, hopefully, ended up with lots of variables and we are seeking a way to keep the top-performing ones. In this post I’m going to explain ‘why’ and ‘how’ we should cleverly select our features.

Model Interpretability with SHAP

Model Interpretability with SHAP

In applied machine learning, there is usually a trade-off between model accuracy and interpretability. Some models like linear regression or decision trees are considered interpretable whereas others, such as tree ensembles or neural networks, are used as black-box algorithms. While this is partly true, there have been great advances in the model interpretability front in…

Read More Read More

2017 season overview

2017 season overview

2017 season is, unfortunately, over and at the same time that was the end of the first season F1-predictor published qualifying and race predictions. In this post I would like to describe my experience from this first year of keeping this blog and – most importantly – provide an objective assessment of 2017 predictions!