- The Lost Art of Decile Analysis - Jul 22, 2021.
The goal of classification is a primary and widely-used application of machine learning algorithms. However, if careful consideration through additional analysis is not taken into the subtlety in the results of an even an apparently straightforward binary classifier, then the deeper meaning of your prediction may be obscured.
Lift charts, Predictive Models, Statistics
- 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.
Big Data, Explainability, Predictive Modeling, Predictive Models, Statistics
- 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.
Pages: 1 2
Business Value, Data Visualization, Lift charts, Predictive Models, Python, R
- 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.
Bayesian, Bayesian Networks, Predictive Models, Probability, Regression, Statistics
- 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.
Feature Engineering, Feature Selection, Predictive Models
- 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.
Competition, Data blending, Kaggle, Logistic Regression, Predictive Models
- 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.
Bootstrap sampling, Dean Abbott, Decile tables, John Elder, Karl Rexer, Lift charts, Predictive Models, Randomization, Target shuffling
