Category: decision-making
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Decisioning at the Edge: Policy Matching at Scale
Decisioning at the Edge: Policy Matching at Scale Policy-to-Agency Optimization with PuLP The post Decisioning at the Edge: Policy Matching at Scale appeared first on Towards Data Science. Erika Gomes-Gonçalves Go to original source
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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
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Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That
Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That From insurance premiums to courtrooms: the impact of noise The post Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That appeared first on Towards Data Science. Sean Moran Go to original source
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Making Smarter Bets: Towards a Winning AI Strategy with Probabilistic Thinking
Making Smarter Bets: Towards a Winning AI Strategy with Probabilistic Thinking Practical guidance on identifying opportunities, managing product portfolios, and overcoming behavioral biases The post Making Smarter Bets: Towards a Winning AI Strategy with Probabilistic Thinking appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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Expected Value Analysis in AI Product Management
Expected Value Analysis in AI Product Management An introduction to key concepts and practical applications The post Expected Value Analysis in AI Product Management appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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AI Agents: From Assistants for Efficiency to Leaders of Tomorrow?
AI Agents: From Assistants for Efficiency to Leaders of Tomorrow? How artificial intelligence is evolving from “simple” assistants to potential architect of our future-even CEOs and governors The post AI Agents: From Assistants for Efficiency to Leaders of Tomorrow? appeared first on Towards Data Science. Luciano Abriata Go to original source
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The Machine, the Expert, and the Common Folks
The Machine, the Expert, and the Common Folks A look at noise, consistency and broken legs The post The Machine, the Expert, and the Common Folks appeared first on Towards Data Science. Lars Nørtoft Reiter Go to original source
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How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes
How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes When numbers lie — and your metrics mislead you The post How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes appeared first on Towards Data Science. Subha Ganapathi Go to original source
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🚪🚪🐐 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…
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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,…
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➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality
➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. This post is targeted towards anyone making data driven…
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Choosing Classification Model Evaluation Criteria
Choosing Classification Model Evaluation Criteria Is Recall / Precision better than Sensitivity / Specificity? Continue reading on Towards Data Science » Viyaleta Apgar Go to original source
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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
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Scale Experiment Decision-Making with Programmatic Decision Rules
Scale Experiment Decision-Making with Programmatic Decision Rules Decide what to do with experiment results in code Photo by Cytonn Photography on Unsplash The experiment lifecycle is like the human lifecycle. First, a person or idea is born, then it develops, then it is tested, then its test ends, and then the Gods (or Product Managers) decide its worth.…
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How Recurrent Neural Networks (RNNs) Are Revolutionizing Decision-Making Research
How Recurrent Neural Networks (RNNs) Are Revolutionizing Decision-Making Research A deep dive into the world of computational modeling and its applications Continue reading on Towards Data Science » Kaushik Rajan Go to original source
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In Defense of Statistical Significance
In Defense of Statistical Significance We have to draw the line somewhere Photo by Siora Photography on Unsplash It’s become something of a meme that statistical significance is a bad standard. Several recent blogs have made the rounds, making the case that statistical significance is a “cult” or “arbitrary.” If you’d like a classic polemic (and…
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When Averages Lie: Moving Beyond Single-Point Predictions
When Averages Lie: Moving Beyond Single-Point Predictions The Case for Predicting Full Probability Distributions in Decision-Making Some people like hot coffee, some people like iced coffee, but no one likes lukewarm coffee. Yet, a simple model trained on coffee temperatures might predict that the next coffee served should be… lukewarm. This illustrates a fundamental problem…
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Why “Statistical Significance” Is Pointless
Why “Statistical Significance” Is Pointless Here’s a better framework for data-driven decision-making Continue reading on Towards Data Science » Samuele Mazzanti Go to original source