Statistics

Beyond ROC-AUC and KS: The Gini Coefficient, Explained Simply

discussed about classification metrics like ROC-AUC and Kolmogorov-Smirnov (KS) Statistic in previous blogs. On this blog, we are going to explore one other vital classification metric called the Gini Coefficient. Why do we've multiple classification...

The Theory of Universal Computation: Bayesian Optimality, Solomonoff Induction & AIXI

In a seminal but underappreciated book titled , Marcus Hutter attempted a mathematical formulation of universal artificial intelligence, shortened to AIXI. This text goals to make AIXI accessible to data scientists, technical enthusiasts and...

Is Your Training Data Representative? A Guide to Checking with PSI in Python

To get essentially the most out of this tutorial, you must have a solid understanding of the right way to compare two distributions. For those who don’t, I like to recommend testing this excellent...

Mastering NLP with spaCy – Part 2

in a sentence provide numerous information, corresponding to what they mean in the true world, how they hook up with other words, how they alter the meaning of other words, and sometimes their...

The Hidden Trap of Fixed and Random Effects

What Are Random Effects and Fixed Effects? When designing a study, we frequently aim to isolate independent variables from those of no interest to watch their true effects on the dependent variables. For instance, let’s...

Easy Guide to Multi-Armed Bandits: A Key Concept Before Reinforcement Learning

make smart decisions when it starts out knowing nothing and may only learn through trial and error? This is strictly what one in all the best but most vital models in reinforcement learning is...

Prescriptive Modeling Makes Causal Bets – Whether You Comprehend it or Not!

modeling is the top of analytics value. It doesn’t give attention to what happened, and even what occur – it takes analytics further by telling us what we should always do to vary...

Exploring the Proportional Odds Model for Ordinal Logistic Regression

You will discover the complete code for this instance at the underside of this post. odds model for ordinal logistic regression was first introduced by McCullagh (1980). This model extends binary logistic regression to situations...

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