Tag: Data Science
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Sketulate: Sketchable Functions & Densities for Data Science
I simulate a lot of data to test my ideas, particularly the more complex ones. With non-standard stuff, it can be pretty time consuming to find the right function and/or parameters to do what I want. If the shape you want isn’t a common function or distribution, you can spend too much time searching for…
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Calibration Plots with Generalized Additive Models (GAMs)
Calibration of probabilistic predictions is a crucial task in many machine learning applications, especially when the outputs are used downstream for decision-making. It’s actually a simple concept, with calibration just ensuring that the predicted probabilities align with the actual outcomes. Despite that, I’ve noticed many people neglect the step in their modelling process! Typically, when…
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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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Learn How to Containerize Your R Notebooks with Docker
In this article I show how to containerize your R Notebooks with Docker, for reproducible sharing, using renv for dependency management.
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Small Data: Creativity, Explainability & Precision
This post discusses the significance of small data in the context of data science and modeling, detailing tips and tricks for working with small data sets.
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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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How to Choose a Distribution for your Regression Model
This article is all about distributions! In it, I explore the most common distributions including Gaussian, Uniform, Student-T, Gamma, and others. I also discuss their applications and when to choose them for regression modelling.
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Working with Heteroscedastic Data
Heteroscedasticity, variance inconsistency across a related variable’s range, is often overlooked in data analysis. The two-part series explores handling heteroscedastic data through transformations, linear models, and model-based solutions, using LinkedIn engagements for instance. Techniques include Quantile Regression, Conditional Variance model, and common transformations like power or square root transformations.
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Hierarchical (Multilevel) Modelling
Hierarchical modeling is a powerful statistical technique to analyze nested or grouped data. It considers both global structure and individual characteristics, delivering more accurate and robust estimates. Hierarchical models outperform traditional machine learning models, providing lower error rates and better handle outliers, especially with small datasets.
Aaron Pickering 