Category: Machine Learning
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XGBoost Can’t Extrapolate
A common pattern that I observe among inexperienced Data Scientists is the following – they often default to XGBoost or a similar Gradient Boosted Model for their problem with any thought as to whether its the right choice for the job. Given how powerful these methods are, this isn’t the most egregious mistake one can…
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Bayesian Time Series Interpolation
The third article in the series, I explore Bayesian modeling for multivariate time series interpolation with hierarchical models with Numpyro and Bambi.
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Genetic Algorithms with PyGAD and PyTorch
Deep dive into Genetic Algorithms (GAs), an optimization algorithm inspired by the concept of natural evolution, including using a GA to train a Pytorch model with the Pygad library.
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Stochastic Time Delay in Regression Analysis
I revisit a previous article on designing a regression model for stochastic time delay problems, where input-output delays vary randomly. The proposed model treats time delay components as part of the analysis, achieving improved results over standard regression methods in simulated experiments. Potential applications include marketing and medical settings. Future extensions might tackle multiple regression…
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Time Series Interpolation using Embeddings & Pytorch
In this article, I use categorical embeddings to tackle time series data interpolation. An algorithmic approach is introduced using Pytorch and I discuss the benefits and drawbacks. Eventually, the creation of a model with these components is demonstrated, as well as the strengths and weaknesses compared to other statistical approaches.
Aaron Pickering 