Have you ever spent weeks fine-tuning a model that just won’t perform, hoping that one more tweak will magically make it work? If so, you might be riding a dead horse. The Dead Horse Theory comes from an old proverb:“When you discover you are riding a dead horse, the best Read more…
In machine learning, there’s a delicate balancing act between bias and variance. Get it wrong, and your model either misses patterns (underfitting) or memorizes noise (overfitting). Finding the sweet spot between the two is key to building models that generalize well. Let’s break it down and explore how techniques like Read more…
Accuracy is often the first metric people check when evaluating machine learning models, but it doesn’t tell the full story. Depending on whether you’re dealing with regression or classification, different metrics provide deeper insights into your model’s performance. Let’s explore the most important evaluation metrics and when to use them. Read more…