A time slicer for training and testing temporally correlated Machine Learning models.
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Updated
Mar 31, 2020 - Python
A time slicer for training and testing temporally correlated Machine Learning models.
Practical guide to validating time-series models: stationary vs non-stationary setups, leakage pitfalls, expanding/rolling TimeSeriesSplit, Ridge/LinearRegression baselines, and bootstrap CIs for metrics. Includes utilities for feature engineering (percent_change) and visualization.
Predict NYC restaurant inspection grades (A vs B/C+) with leak-safe, time-split ML baselines.
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