Latest
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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.
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Simple vs. Stratified Sampling What's the Difference?
In research, the best data you can feed a model is the whole population. After a decade of working with data, working with the entire population is almost never the case. For most of the analyses we create, we end up working with a sample.
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The Statistical Trick That Lets 1,000 People Represent Millions
Growing up, watching the first presidential elections after the military dictatorship in Brazil caught my attention. I watched a very young governor take the lead in the general elections
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Central Limit Theorem
The central limit theorem (CLT) is, arguably, the most important theorem in statistics. It is, in many cases, an introductory statistics course.
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All Models Are Wrong!
”All models are wrong, but some are useful.” Charles Box
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What Universal Optmization Works Best? No-Free-Lunch Theorem
There are hundreds, if not thousands, of algorithms and statistical methods. When I first started in this field, the first method I tried in every single dataset was K-means. It was my go-to algorithm.
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Unweighted and Weighted Logistic Regression Models
Even after so many years, I still remember one of my very first logistic regression projects. I was so proud of it. Until my supervisor kicked it back and told me that I had to weigh it.
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The AI Bubble Nobody Wants to Talk About
There isn't one day when I open the news or social media that I don't come across AI news.
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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.
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Why P-Values Don't Mean What Most People Think They Mean
Very early in my PhD program, I began working with and testing hypotheses. One of the first challenges I had to overcome was understanding the p-value.
