This article details a hands-on approach to modeling rare events in time series data using Python. It covers data exploration, defining extreme events, fitting distributions (GEV, Weibull, Gumbel), and evaluating model performance using metrics like log-likelihood, AIC, and BIC. The example uses weather data and provides code snippets for implementation.
This article explores the impact of hyperparameters on random forests, both in terms of performance and visual representation. It compares the performance of a default random forest with tuned decision trees and examines the effects of various hyperparameters like `n_estimators`, `max_depth`, and `ccp_alpha` using visualizations of individual trees, predictions, and errors.
Learn how to connect several essential tools to develop a simple yet intuitive dashboard using Streamlit, Plotly, DuckDB, and Pandas to visualize data from a JSON file.
This article explores gamma spectroscopy using a Radiacode 103G detector and Python, detailing data collection, analysis, and experiments with various objects to identify radioactive elements.
