Decision

, we explored how a Decision Tree Regressor chooses its optimal split by minimizing the Mean Squared Error (MSE). Today for Day 7 of the Machine Learning “Advent Calendar”, we proceed the identical approach but...

5 days of this Machine Learning “Advent Calendar”, we explored 5 models (or algorithms) which are all based on distances (local Euclidean distance, or global Mahalanobis distance). So it's time to change the approach,...

machine learning algorithms can’t handle categorical variables. But decision trees (DTs) can. Classification trees don’t require a numerical goal either. Below is an illustration of a tree that classifies a subset of Cyrillic...

Data shouldn't be just an asset; it has develop into the lifeblood of companies today, driving all the things from each day decisions to long-term strategy, and is central to competitiveness and innovation. It’s...

A journey into three intuitions: Common, Bayesian and CausalThe Monty Hall Problem is a widely known brain teaser from which we are able to learn necessary lessons in decision making which are useful basically...

A way to raised allow decision trees for use as interpretable modelsWhile decision trees can often be effective as interpretable models (they're quite comprehensible), they depend on a greedy approach to construction that may...

Introducing a recent model-agnostic, post hoc XAI approach based on CART to supply local explanations improving the transparency of AI-assisted decision making in healthcareWithin the realm of artificial intelligence, there may be a growing...

Boosting Your Method to SuccessImagine running a relay race. Each runner improves upon the previous one’s performance, and together, they win the race. That’s how these algorithms work — every latest model compensates for...