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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.
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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.
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Why Data Science Projects Fail and How to Avoid It
Not long ago, I sat with an apartment owner who had taken over 144 units. There were 15 vacant, 12 on move-out notice, and four evictions for the end of the month. If the notices and evictions were to go through, the occupancy rate would be at 78 percent.
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Amazon Ships Before You Order—Here's How Your Business Can Do the Same
Amazon ships products before you order them. And it's been doing it for over a decade.
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Property Files #2: When the System Fails Everyone
I debated whether to share this story. Evictions are one of the most difficult parts of property management—and one of the least understood or perceived as corporate greed. From the outside, it looks simple: the resident doesn't pay, the landlord kicks them out. Landlord wins, residents lose.
