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Category: Predictive Modelling

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 objects they represent. In the end of this post, you will understand how all this relates to Alonso’s driving skills. Please share the post:

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 you build. In this post I’ll start by showing the most common evaluation metrics and then reveal the custom evaluation metric I use for assessing…

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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 the past few months. In this post I will explain one I the newest methods in the field, the so-called SHAP, and show some practical…

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