Introduction
My previous posts checked out the bog-standard decision tree and the wonder of a random forest. Now, to finish the triplet, I’ll visually explore !
There are a bunch of gradient boosted tree libraries, including...
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...
Scientific publication
T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by determining the optimal variety of trees. , 26(1), 95.Follow this LINK to the unique publication.
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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...
If you've gotten read my previous articles on Gradient Boosting and Decision Trees, you're aware that Gradient Boosting, combined with Ensembles of Decision Trees, has achieved excellent performance in classification or regression tasks involving...
If you may have read my previous articles on Gradient Boosting and Decision Trees, you might be aware that Gradient Boosting, combined with Ensembles of Decision Trees, has achieved excellent performance in classification or...