TTharunintharunpandya.hashnode.dev·1d ago · 6 min readLogistic Regression: The Workhorse of Yes or No ProblemsRemember the Swiggy late-order problem from a couple posts ago? Ten orders, distance in kilometers, and one simple label: was it late, yes or no. We tried fitting our usual straight line to it, and it10
TTharunintharunpandya.hashnode.dev·Sep 19 · 7 min readThe Sigmoid Function: Squashing the World Into 0 and 1You open YouTube. You haven't searched for anything, yet the very first video looks strangely interesting. You click. Imagine a simplified recommendation model working behind the scenes, quietly askin10
TTharunintharunpandya.hashnode.dev·Aug 31 · 9 min readFrom Lines to Decisions: Why Regression Fails at ClassificationLast time, right at the end, I asked a question and then walked away from it. Will this order be late, or won't it? The final answer isn't a number on a continuous scale. It's a class: yes or no. Ever10
TTharunintharunpandya.hashnode.dev·Aug 22 · 10 min readPolynomial Regression: Teaching a Line to BendTake our best straight-line model so far, the one that fit our five-order table perfectly. Now widen the range a bit. Some orders close by, a couple kilometers away. Some far, ten or twelve kilometers10
TTharunintharunpandya.hashnode.dev·Aug 19 · 10 min readFeature Scaling in Machine Learning: Why It MattersTake the exact same model, the exact same data, the exact same code. Change one thing: write the distance column in meters instead of kilometers. Nothing else moves. Run it, and the model doesn't just10
