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Tag: decision

  • Decision-Focused Sequential Experimental Design: A Directional Uncertainty-Guided Approach

    Decision-Focused Sequential Experimental Design: A Directional Uncertainty-Guided Approach arXiv:2602.05340v1 Announce Type: new Abstract: We consider the sequential experimental design problem in the predict-then-optimize paradigm. In this paradigm, the outputs of the prediction model are used as coefficient vectors in a downstream linear optimization problem. Traditional sequential experimental design aims to control the input variables (features)…

  • Multi-Attribute Decision Matrices, Done Right

    Multi-Attribute Decision Matrices, Done Right How to structure decisions, identify efficient options, and avoid misleading value metrics The post Multi-Attribute Decision Matrices, Done Right appeared first on Towards Data Science. Josiah DeValois Go to original source

  • The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel

    The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel Gradient descent in function space with decision trees The post The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel appeared first on Towards Data Science. angela shi Go to original source

  • The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

    The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how…

  • The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor

    The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees. With a simple one-feature dataset, we can see how a tree chooses its first split. The idea…

  • When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making

    When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making arXiv:2510.07750v1 Announce Type: new Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches…

  • 3 Greedy Algorithms for Decision Trees, Explained with Examples

    3 Greedy Algorithms for Decision Trees, Explained with Examples Learn the inner workings of decision trees The post 3 Greedy Algorithms for Decision Trees, Explained with Examples appeared first on Towards Data Science. Kuriko Iwai Go to original source

  • Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation

    Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation arXiv:2508.20942v1 Announce Type: new Abstract: In this paper, we extend the transfer learning classification framework from regression function-based methods to decision rules. We propose a novel methodology for modeling posterior drift through Bayes decision rules. By exploiting the geometric…

  • A Visual Guide to Tuning Decision-Tree Hyperparameters

    A Visual Guide to Tuning Decision-Tree Hyperparameters How hyperparameter tuning visually changes decision trees The post A Visual Guide to Tuning Decision-Tree Hyperparameters appeared first on Towards Data Science. James Gibbins Go to original source

  • Bayesian preference elicitation for decision support in multiobjective optimization

    Bayesian preference elicitation for decision support in multiobjective optimization arXiv:2507.16999v1 Announce Type: new Abstract: We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesian model to estimate the decision-maker’s utility function based on pairwise comparisons. Aided by this model,…

  • Decision Trees Natively Handle Categorical Data

    Decision Trees Natively Handle Categorical Data But mean target encoding is their turbocharger The post Decision Trees Natively Handle Categorical Data appeared first on Towards Data Science. Vadim Arzamasov Go to original source

  • Understanding Random Forest using Python (scikit-learn)

    Understanding Random Forest using Python (scikit-learn) Decision trees are a popular supervised learning algorithm with benefits that include being able to be used for both regression and classification as well as being easy to interpret. However, decision trees aren’t the most performant algorithm and are prone to overfitting due to small variations in the training…

  • 🚪🚪🐐 Lessons in Decision Making from the Monty Hall Problem

    🚪🚪🐐 Lessons in Decision Making from the Monty Hall Problem The Monty Hall Problem is a well-known brain teaser from which we can learn important lessons in Decision Making that are useful in general and in particular for data scientists. If you are not familiar with this problem, prepare to be perplexed . If you…

  • Bayesian learning of the optimal action-value function in a Markov decision process

    Bayesian learning of the optimal action-value function in a Markov decision process arXiv:2505.01859v1 Announce Type: new Abstract: The Markov Decision Process (MDP) is a popular framework for sequential decision-making problems, and uncertainty quantification is an essential component of it to learn optimal decision-making strategies. In particular, a Bayesian framework is used to maintain beliefs about…

  • Formalising Anti-Discrimination Law in Automated Decision Systems

    Formalising Anti-Discrimination Law in Automated Decision Systems arXiv:2407.00400v2 Announce Type: cross Abstract: Algorithmic discrimination is a critical concern as machine learning models are used in high-stakes decision-making in legally protected contexts. Although substantial research on algorithmic bias and discrimination has led to the development of fairness metrics, several critical legal issues remain unaddressed in practice.…

  • Centroid Decision Forest

    Centroid Decision Forest arXiv:2503.19306v1 Announce Type: new Abstract: This paper introduces the centroid decision forest (CDF), a novel ensemble learning framework that redefines the splitting strategy and tree building in the ordinary decision trees for high-dimensional classification. The splitting approach in CDF differs from the traditional decision trees in theat the class separability score (CSS)…

  • Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits

    Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits arXiv:2503.00273v1 Announce Type: new Abstract: We study the evolution of information in interactive decision making through the lens of a stochastic multi-armed bandit problem. Focusing on a fundamental example where a unique optimal arm outperforms the rest by a fixed margin, we…

  • A Review of Causal Decision Making

    A Review of Causal Decision Making arXiv:2502.16156v1 Announce Type: new Abstract: To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision-making through a causal lens: 1) the discovery of causal relationships through causal structure…

  • The Future of Data: How Decision Intelligence is Revolutionizing Data

    The Future of Data: How Decision Intelligence is Revolutionizing Data In the past few years, technology and AI have evolved more than ever. As I read about the new concepts in tech and learn new skills and techniques each day, I feel in a state of limbo — there is so much content to consume and yet,…

  • Build a Decision Tree in Polars from Scratch

    Build a Decision Tree in Polars from Scratch Decision Tree algorithms have always fascinated me. They are easy to implement and achieve good results on various classification and regression tasks. Combined with boosting, decision trees are still state-of-the-art in many applications. Frameworks such as sklearn, Lightgbm, xgboost and catboost have done a very good job…

  • Data-Driven Decision Making with Sentiment Analysis in R

    Data-Driven Decision Making with Sentiment Analysis in R Leveraging the Quanteda, Textstem and Sentimentr Packages to Extract Customer Insights and Enhance Business Strategy Continue reading on Towards Data Science » Devashree Madhugiri Go to original source

  • SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules

    SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules arXiv:2501.09900v1 Announce Type: new Abstract: Bayesian Additive Regression Trees [BART, Chipman et al., 2010] have gained significant popularity due to their remarkable predictive performance and ability to quantify uncertainty. However, standard decision tree models rely on recursive data splits at each decision node, using…

  • Towards understanding the bias in decision trees

    Towards understanding the bias in decision trees arXiv:2501.04903v1 Announce Type: new Abstract: There is a widespread and longstanding belief that machine learning models are biased towards the majority (or negative) class when learning from imbalanced data, leading them to neglect or ignore the minority (or positive) class. In this study, we show that this belief…

  • Bridging the Data Literacy Gap

    Bridging the Data Literacy Gap The Advent, Evolution, and Current state of “Data Translators” Introduction With Data being constantly glorified as the most valuable asset organizations can own, leaders and decision-makers are always looking for effective ways to put their data insights to use. Every time customers interact with digital products, millions of data points…