Part 2 of the tutorial on how to run and evaluate experiments without leaving your IDE — In the previous article of this series, I demonstrated how to use DVC’s VS Code extension to transform our IDE into an experimentation platform, allowing us to directly run and evaluate ML experiments. I also mentioned that the extension offers useful plotting functionalities, which enable us to visualize and evaluate…

Learn how to quickly create a presentation-ready plot to aid your data storytelling — Waterfall plots (or charts) are frequently used to demonstrate a cumulative change in a certain value over time. Alternatively, they can use fixed categories (for example, certain events) instead of time. …

How to run and evaluate experiments without leaving your IDE — One of the biggest threats to productivity in recent times is context switching. It is a term originating from computer science but applied to humans it refers to the process of stopping work on one thing, performing a different task, and then picking back up the initial task. During a…

With a hands-on example of using Huber, RANSAC and Theil-Sen regression algorithms — Linear regression is one of the simplest machine learning models out there. It is often the starting point not only for learning about data science but also for building quick and simple minimum viable products (MVPs), which then serve as benchmarks for more complex algorithms. In general, linear regression fits…

Shiny is joining the ranks of web app tools such as Streamlit and Dash — Recently RStudio announced that the company is going to change its name to Posit (meaning: to propose an idea or theory) to reflect the company’s plans to expand its focus beyond R. Their efforts will now also cover Python users and integration with Visual Studio Code. However, R users were…

Using dummy variables, cyclical encoding and radial basis functions — Imagine you have just started a new data science project. The goal is to build a model predicting Y, the target variable. You have already received some data from the stakeholders/data engineers, did a thorough EDA, and selected some variables you believe are relevant for the problem at hand. Then…

Learn what is the impact of the popular resampling approaches to dealing with class imbalance — When dealing with imbalanced data, one of the go-to approaches is to resample the training data to reduce the class imbalance. This can involve undersampling the majority class, oversampling the minority class, or a combination of both. …

Is it even possible? How should you approach it? — Deploying machine and deep learning models to production is definitely a very satisfying moment for each data scientist working on the project. However, by no means it is the project’s last step. …