-
Imbalanced classes: why 99% accuracy can mean your model is useless
Imagine building a fraud detection system that achieves 99% accuracy . Your dashboard looks excellent. Your stakeholders are impressed. The model passes testing and gets deployed.
-
SVMs and the kernel trick: separating the inseparable
Many machine-learning problems become easier once you stop thinking first about algorithms and start thinking about geometry.
-
Training models with Spark MLlib: LogisticRegression, RandomForest, and GBTClassifier
When your data grows beyond the memory of a single machine, moving it into pandas or NumPy just to train a model becomes expensive. Apache Spark’s spark.ml library keeps feature engineering, model tra
-
Logistic regression as a classifier you can actually explain
Machine learning often feels like a trade-off between accuracy and interpretability. While deep neural networks and gradient-boosted trees dominate many benchmarks, there are plenty of real-world prob