Filed under machine-learning
-
What is Prediction Error?
No model hits the nail on the head all the time. It is just not possible. It can predict something with a certain level of confidence. It is better than perception. At any point, the predictions could be wrong. To determine the valid…
-
What a Used Car Prediction Can Teach You About Machine Learning
Machine Learning
Machine learning, a form or artificial intelligence, has been used for many years to help with prediction by analysing historical data, recognize patterns, and forecast future outcomes.
-
Can a Human Being Live to 300 Years? A Machine Learning Exercise
The answer has been long sought by more than scientists and doctors. In fact, the first person who had the idea to record death was neither a scientist or medical doctor. It was a statistian: John Graunt
-
The Future of Biological Data Analysis
A deep-learning framework reveals whole-body perturbations at cell level, 2026
Machine Learning (ML) is often associated with predictions. And that is the case most of the time. However, its use expand beyond predictions. A new paper published in Nature caught my attention.
-
Precision, Recall, and F1 Score Metrics. Which one Matters Most?
In the statistical world, in my simple mind, precision was the golden metric for gauging whether your model was performing as it was supposed to. That is partially correct. But precision alone will only give you a partial image of the model's performance. For a full picture, you will need to check other gauges — Recall and F1 Score.
-
What Are Vector Embeddings?
Is vector embedding one of those buzzwords in data science? What are they and why are they important? In short, they are decoders or translators, essential for machine learning.
-
What is Bias and Variance Tradeoff
You don't want the model to over- or under-fit. You have to strive to find the right balance between the two. That is the bias-variance tradeoff.
About
I am a veteran. I spent years in intelligence analysis, where being confidently wrong carried a cost, and that is where I learned to separate what evidence shows from what it suggests. I have a master's in journalism and communications, and I am finishing a doctorate in data science on how findings get communicated. I work in English and Portuguese.