- Many Heads Are Better Than One: The Case For Ensemble Learning - Sep 13, 2019.
While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.
- Clearing air around “Boosting” - Jun 3, 2019.
We explain the reasoning behind the massive success of boosting algorithms, how it came to be and what we can expect from them in the future.
- Ensemble Learning: 5 Main Approaches - Jan 3, 2019.
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.
- Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
- What is the difference between Bagging and Boosting? - Nov 6, 2017.
Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail.
- Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
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- The Machine Learning Algorithms Used in Self-Driving Cars - Jun 19, 2017.
Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.
- 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
- Data Science Basics: An Introduction to Ensemble Learners - Nov 8, 2016.
New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.
- Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results - Feb 12, 2016.
Get a handle on ensemble methods from voting and weighting to stacking and boosting, with this well-written overview that includes numerous Python-style pseudocode examples for reinforcement.
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- Lessons from 2 Million Machine Learning Models on Kaggle - Dec 24, 2015.
Lessons from Kaggle competitions, including why XG Boosting is the top method for structured problems, Neural Networks and deep learning dominate unstructured problems (visuals, text, sound), and 2 types of problems for which Kaggle is suitable.
- Are you trying to acquire Machine Learning Skills? - Sep 16, 2015.
Embarking on a journey through the lands of machine learning? Here are few important lessons like Feature Engineering, Model tuning, Overfitting, Ensembling etc. which you should keep in mind along the way.
- Top 10 Data Mining Algorithms, Explained - May 21, 2015.
Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.
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- Sibyl: Google’s system for Large Scale Machine Learning - Aug 20, 2014.
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
- Gaming Analytics Summit 2014: Day 1 Highlights - May 29, 2014.
Highlights from the presentations by Gaming Analytics experts from Activision, Valve, Microsoft and Broken Bulb Studios on Day 1 of Gaming Analytics Summit 2014.
- Identity Fraud and Analytics – An Overview - Mar 26, 2014.
With the consumers being increasingly concerned about identity theft, leading financial institutions are leveraging analytics to detect Identity Fraud as it happens.
- Building Better Models – Case studies in Predictive Analysis – JMP Webcasts - Jan 27, 2014.
The five-part on-demand series shows how to build better and more useful models with modern predictive modeling techniques such as regression, neural networks and decision trees.