Tag: machine-learning
All the articles with the tag "machine-learning".
Features and Overfitting: How a Model Memorizes, and How L1 and L2 Stop It
Updated:Give the router 379 features instead of one and it scores 100% on its training data and worse on new questions. Why that happens, why the weights run to infinity, how L2 and L1 penalties stop them (derived, with the soft-threshold that makes L1 exact zeros), why one well-chosen feature matches all 379, and how to tell a real difference from noise when the splits share data.
Learning Is Optimization: Linear and Logistic Regression, Trained by Gradient Descent
Updated:What it actually means for a model to learn: pick a loss, then walk downhill. Worked end to end on real data from this blog, with the loss choice, the closed-form fit, gradient descent and its learning-rate limit, and a first logistic-regression router whose gradient reappears in every later post.
Measuring a Classifier Honestly: Precision, Recall, Thresholds and Calibration
Updated:An 88.5% accurate router that misses 38% of the questions it exists to catch. Why accuracy hides that, how precision, recall and ranking metrics expose it, how to choose a threshold from what each mistake costs without leaking the test set, whether the router's probabilities mean what they say, and what happens when Vietnamese questions arrive.