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Feature Subset Optimization I: Search Space Modeling and Feasibility Checks
Case setup: A classifier has n candidate features. Each feature can be included or excluded, so the...
Playing Cards with OOP: Card and Deck
Problem setup: Build a reusable model of a standard card deck for simulation and probability tasks....
Route Optimization I: Domain Modeling, Constraints, and Local vs Global Decisions
Case setup: A route from Winterthur to Kreuzlingen is split into legs. Each leg has several...
Feature-Set Experiments with OOP
Problem setup: A Data Science team has many candidate features but can train only a subset per run...
itertools in Data Science: Product, Combinations, Permutations
The itertools module provides iterator tools for combinatorial generation. Typical Data Science...
Classes, Objects, Constants, and Magic Methods
Object-oriented programming is most useful when each class models one domain concept with clear...
Damage Regression I: Data Ingestion, Cleaning, and Vocabulary Mapping
Case setup: City-report platforms receive free-text damage descriptions with noisy punctuation and...
Route Optimization II: Search Space, Brute Force, and Practical Heuristics
For a multi-leg trip, each leg can offer several modalities. If each of L legs has k options,...
Damage Regression II: Combinatorial Feature Expansion and Baseline Prediction
After extraction, each report is represented by up to 3 mapped token IDs. The lecture case expands...
Feature Subset Optimization II: Heuristics, Baselines, and Validation Discipline
When exhaustive search is too expensive, a common practical approach is greedy forward selection....
DS Analogue of Card Deck: Feature Bundle Generator
A domain-specific analogue to card dealing for Data Scientists: Instead of suits and ranks, we have...
Measurement and Born Rule
You prepared a superposition and measured once: the histogram you see is not the wavefunction, it...