Michelle Fullwood - Grids, Streets & Pipelines: Making a linguistic streetmap with scikit-learn
392 views · Published 9 December 2014 · 42:59 · Indexed 23 September 2026
Channel: PyData · 2014 · Science & Technology
PyData NYC 2014 This talk is aimed at scikit-learn novices who have built their first classifier with out-of-the-box features, been disappointed with the results, and wondered what to do for a next step. As a case study, I'll use a personal project to classify streetnames in Singapore according to their language of origin, which I then turned into a colour-coded streetmap. We'll build a baseline classifier using OpenStreetMap data, GeoPandas, and scikit-learn, then explore how to add your own feature Pipelines and how to tune hyperparameters using GridSearchCV, including how to pick a parameter grid. Lastly we'll plot the map and review which method of improving the baseline classifier worked best: more data, adding features, hyperparameter tuning, or swapping out classifiers? 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
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