Apr 7 · 9 min read · The efficiency of an LLM memory access pattern directly determines an agent's ability to retrieve and use stored knowledge, impacting everything from conversational coherence to complex problem-solving. In 2023, a major AI research paper cited that o...
Join discussionOct 15, 2025 · 4 min read · Precision :→ Intuition :→ Imagine we are building a model that predicts whether the mail is spam or not , For e.g if my model predicts that my mail is spam . So how true that statement is , this is precision. In other words , we can say that out o...
Join discussionJul 14, 2025 · 3 min read · “A good model isn't just accurate — it's understood.”— Tilak Savani 🧠 Introduction After training a model, we can’t just stop at accuracy — especially when dealing with imbalanced datasets or real-world problems like spam detection, medical diagn...
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Apr 19, 2025 · 3 min read · Introduction: In the world of machine learning, selecting the right model for your dataset can often feel like a game of trial and error. For this blog, I’m diving into the Sonar rock vs mine classification problem, where I tested various machine lea...
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Dec 22, 2024 · 6 min read · [26] Introduction A Confusion Matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning ...
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Dec 9, 2024 · 4 min read · We have already discussed accuracy as a metric, its limitations and confusion matrix in the previous post in the series. This post will cover the metrics that we can derive from confusion matrix and how they serve as a better alternative than looking...
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Nov 11, 2024 · 10 min read · Hi there, in this post, I will discuss the metrics that are used to evaluate how accurate machine learning models are. I hope that helps. You can use this page as a cheat sheet. At its core, what we all do when deriving a machine learning model is ma...
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