- Evidence Counterfactuals for explaining predictive models on Big Data - May 18, 2020.
Big Data generated by people -- such as, social media posts, mobile phone GPS locations, and browsing history -- provide enormous prediction value for AI systems. However, explaining how these models predict with the data remains challenging. This interesting explanation approach considers how a model would behave if it didn't have the original set of data to work with.
- Modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models - Jun 21, 2019.
Explaining the business value of your predictive models to your business colleagues is a challenging task. Using Modelplotr, an R package, you can easily create stunning visualizations that clearly communicate the business value of your models.
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- Evaluating the Business Value of Predictive Models in Python and R - Oct 11, 2018.
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.
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- Make Your Models a Competitive Advantage - May 31, 2018.
The Model Management white paper, based on our experience working with hundreds of model-driven organizations, describes the reasons most organizations have not yet unlocked the transformative potential of models and provides a framework for success.
- Advancing Your Analytics Career With Automated Machine Learning - Apr 5, 2018.
Join DataRobot on Apr 26 at 1:00 pm EST for this webinar, in which industry expert Jen Underwood will show how you can use automated machine learning to quickly develop predictive models and advance your career beyond traditional business intelligence.
- The amazing predictive power of conditional probability in Bayes Nets - Nov 13, 2017.
This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.
- How Bayesian Networks Are Superior in Understanding Effects of Variables - Nov 9, 2017.
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
- KDnuggets™ News 17:n31, Aug 16: Data Science Primer: Basic Concepts; Python vs R vs rest - Aug 16, 2017.
Also: What Artificial Intelligence and Machine Learning Can Do-And What It Can't; How Convolutional Neural Networks Accomplish Image Recognition?; Making Predictive Models Robust: Holdout vs Cross-Validation; The Machine Learning Abstracts: Support Vector Machines
- Data Mining Tip: How to Use High-cardinality Attributes in a Predictive Model - Aug 29, 2016.
High-cardinality nominal attributes can pose an issue for inclusion in predictive models. There exist a few ways to accomplish this, however, which are put forward here.
- Using Ensembles in Kaggle Data Science Competitions- Part 3 - Jun 27, 2015.
Earlier, we showed how to create stacked ensembles with stacked generalization and out-of-fold predictions. Now we'll learn how to implement various stacking techniques.
- ASE International Conference on Big Data Science 2014: Day 1 Highlights - Aug 1, 2014.
Highlights from the presentations by Data Science leaders from Pivotal, IBM Research, George Washington University, IARPA at ASE Conference on Big Data Science 2014 held in Stanford University.
- ASE International Conference on Big Data Science 2014: Highlights from Workshops - Jul 31, 2014.
Highlights from the presentations by Data Science leaders from MIT, Georgia Tech, Microsoft Research and CUHK during workshops at ASE Conference on Big Data Science 2014 held in Stanford University.
- Wharton Conference: Successful Applications Of Customer Analytics, May 1 - Mar 14, 2014.
As a research center at the intersection between academics and industry, WCAI will be showcasing presentations that illustrate a high level of rigor but are also broadly accessible to practitioners, with case studies from top companies including MGM Resorts, Cleveland Indians, Pfizer/Kaggle, GE Capital.
- Top stories for Feb 9-15: Cartoon: Data Scientist Valentine Day Prediction; 3 Ways to Test the Accuracy of Predictive Models - Feb 16, 2014.
Cartoon: Data Scientist Valentine Day Prediction; 3 Ways to Test the Accuracy of Your Predictive Models; One Page R: A Survival Guide to Data Science with R; Book: Mining of Massive Datasets, 2nd Edition, free download.
- Top KDnuggets tweets, Feb 7-9: 3 ways to test predictive models; 90% of top-paying IT jobs Big Data related - Feb 10, 2014.
3 ways to test Predictive Models accuracy; 90% of top-paying IT jobs are related to #BigData, R; 10 Emerging Analytics Startups in India ; CMSR Data Miner/Rule-Engine Software - free academic use.
- 3 Ways to Test the Accuracy of Your Predictive Models - Feb 8, 2014.
3 different methods for testing accuracy of predictive models from 3 leading analytics experts - Karl Rexer, John Elder, and Dean Abbott explain using lift charts, randomization testing, and bootstrap sampling.
- Webcast: BigML Programmatic Machine Learning Made Easy, Jan 28 - Jan 9, 2014.
BigML Winter release offers enhanced performance and many new features for quickly building powerful predictive models, applications and services. Learn more on Jan 28 and get a 25% discount.