Articles by The Pleasure of Finding Things Out: A blog by James Triveri

GIS Algorithms in Python, Part I – Point-to-Point and Point-to-Line Distances

November 22, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

I’ve been spending time exploring Ningchuan Xiao’s excellent book, GIS Algorithms. This text offers a comprehensive dive into fundamental geospatial concepts including geometric algorithms, spatial indexing, and spatial analysis. The author thor...

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The Bootstrap Chain Ladder from Scratch in Python

November 5, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

The bootstrap chain ladder is a statistical technique used in actuarial reserving to project future claim liabilities based on historical data. It builds upon the chain ladder method, but the chain ladder method by itself does not provide a meas...

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Determining Distance to Coastline for Policy Locations Using GeoPandas

October 14, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

Knowing the distance to coastline for an exposure is crucial for insurance rating applications because it helps insurers assess the risk of hazards like hurricanes, storm surges, and flooding, which are much more prevalent in coastal areas. This...

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A Data-Driven Approach to Ranking Teams in Uneven Paired Competition

October 4, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

I recently came across The Perron-Frobenius and the Ranking of Football Teams, an interesting paper in which the author describes four different methods to rank teams in uneven paired competition. He goes on to show how each of these methods dep...

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Techniques for Convolutional Neural Network Interpretability

October 3, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

Saliency maps for Convolutional Neural Networks are a technique used to visualize and understand the behavior of CNN models, particularly in tasks such as image classification, object detection and segmentation. They highlight which parts of an ...

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Introduction to Markov Chain Monte Carlo – The Metropolis-Hastings Algorithm

September 21, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

Markov Chain Monte Carlo (MCMC) is a class of algorithms used to sample from probability distributions when direct sampling is difficult or inefficient. It leverages Markov chains to explore the target distribution and Monte Carlo methods to per...

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Introduction to Folium

September 1, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

Learning geospatial data science is crucial in today’s data-driven world for several reasons. Geospatial data science enables individuals to understand and analyze complex spatial phenomena, including natural disasters, urbanization, climate cha...

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Working with Geospatial Vector Data

August 17, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

Geospatial vector data is a way of representing geographic features in a digital format using points, lines, and polygons. Unlike raster data, which represents geographic data as a grid of cells or pixels, vector data represents features more pr...

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Working with Geospatial Vector Data

August 17, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

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Composite Estimators in scikit-learn

August 1, 2024 | The Pleasure of Finding Things Out: A blog by James Triveri

To build a composite estimator in scikit-learn, transformers are usually combined with other transformers and/or predictors (such as classifiers or regressors). The most common tool used for composing estimators is a Pipeline. The Pipeline is often used in combination with ColumnTransformer or FeatureUnion which concatenate the output of transformers ... [...Read more...]

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Articles by The Pleasure of Finding Things Out: A blog by James Triveri

GIS Algorithms in Python, Part I – Point-to-Point and Point-to-Line Distances

The Bootstrap Chain Ladder from Scratch in Python

Determining Distance to Coastline for Policy Locations Using GeoPandas

A Data-Driven Approach to Ranking Teams in Uneven Paired Competition

Techniques for Convolutional Neural Network Interpretability

Introduction to Markov Chain Monte Carlo – The Metropolis-Hastings Algorithm

Introduction to Folium

Working with Geospatial Vector Data

Working with Geospatial Vector Data

Composite Estimators in scikit-learn