app.py - Visualizing Uncertainty in Models using Pandas, Scikit Learn, Jinja, Flask, and D3.js

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app.py - Visualizing Uncertainty in Models using Pandas, Scikit Learn, Jinja, Flask, and D3.js

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Last edited Jul 10, 2019

Created on Jul 7, 2019

This example predicts the conference category of paper abstracts using a Naive Bayes classifier, then displays the model’s classification report to visualize prediction uncertainty. The Flask app loads a CSV of IEEE abstracts, vectorizes them with CountVectorizer, trains a MultinomialNB model, and renders the report via Jinja templates. D3.js is referenced for potential charting, though the visible code uses Pandas, scikit-learn’s classification_report, and Flask request handling to show precision, recall, and F1-scores per category.

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