- LightGBM: A Highly-Efficient Gradient Boosting Decision Tree - Jun 18, 2020.
LightGBM is a histogram-based algorithm which places continuous values into discrete bins, which leads to faster training and more efficient memory usage. In this piece, we’ll explore LightGBM in depth.
- 5 Great New Features in Scikit-learn 0.23 - May 15, 2020.
Check out 5 new features of the latest Scikit-learn release, including the ability to visualize estimators in notebooks, improvements to both k-means and gradient boosting, some new linear model implementations, and sample weight support for a pair of existing regressors.
- 5 Great New Features in Latest Scikit-learn Release - Dec 10, 2019.
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
- 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.
- XGBoost Algorithm: Long May She Reign - May 2, 2019.
In recent years, XGBoost algorithm has gained enormous popularity in academic as well as business world. We outline some of the reasons behind this incredible success.
- Mastering Fast Gradient Boosting on Google Colaboratory with free GPU - Mar 19, 2019.
CatBoost is a fast implementation of GBDT with GPU support out-of-the-box. Google Colaboratory is a very useful tool with free GPU support.
- Understanding Gradient Boosting Machines - Feb 6, 2019.
However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.
- Building AI to Build AI: The Project That Won the NeurIPS AutoML Challenge - Jan 23, 2019.
This is an overview of designing a computer program capable of developing predictive models without any manual intervention that are trained & evaluated in a lifelong machine learning setting in NeurIPS 2018 AutoML3 Challenge.
- KDnuggets™ News 18:n44, Nov 21: What is the Best Python IDE for Data Science?; Anticipating the next move in data science - Nov 21, 2018.
Also: Mastering The New Generation of Gradient Boosting; Top 10 Python Data Science Libraries; Predictive Analytics in 2018: Salaries & Industry Shifts; Sorry I didn't get that! How to understand what your users want; Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU
- 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.
- Introduction to Fraud Detection Systems - Aug 17, 2018.
Using the Python gradient boosting library LightGBM, this article introduces fraud detection systems, with code samples included to help you get started.
- Unveiling Mathematics Behind XGBoost - Aug 14, 2018.
Follow me till the end, and I assure you will atleast get a sense of what is happening underneath the revolutionary machine learning model.
- Intuitive Ensemble Learning Guide with Gradient Boosting - Jul 30, 2018.
This tutorial discusses the importance of ensemble learning with gradient boosting as a study case.
- CatBoost vs. Light GBM vs. XGBoost - Mar 22, 2018.
Who is going to win this war of predictions and on what cost? Let’s explore.
- 5 Machine Learning Projects You Should Not Overlook - Feb 8, 2018.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- KDnuggets™ News 18:n04, Jan 24: TensorFlow vs XGBoost; Machine Learning Pipelines in Python; Semi-Supervised Machine Learning - Jan 24, 2018.
Gradient Boosting in TensorFlow vs XGBoost; Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2; Using Genetic Algorithm for Optimizing Recurrent Neural Networks; The Value of Semi-Supervised Machine Learning; Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI
- Gradient Boosting in TensorFlow vs XGBoost - Jan 18, 2018.
For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today.
- Top KDnuggets tweets, Jan 10-16: The Art of Learning #DataScience; Gradient Boosting in #TensorFlow vs XGBoost - Jan 17, 2018.
Also Japanese scientists just used #AI #DeepLearning to read minds and it's amazing; Using #DeepLearning to Solve Real World Problems.
- Top KDnuggets tweets, Dec 27 – Jan 02: 10 Free Must-Read Books for #MachineLearning and #DataScience - Jan 3, 2018.
Also #TensorFlow: A proposal of good practices for files, folders and models; Creating REST API for #TensorFlow models; The Most Popular Language For #MachineLearning and #DataScience Is ...
- Understanding Machine Learning Algorithms - Oct 3, 2017.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
- Getting Up Close and Personal with Algorithms - Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
- 7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
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- Webinar: Improve Your Regression with CART and Gradient Boosting, Feb 16 - Feb 13, 2017.
Learn about a powerful tree-based machine learning algorithm called gradient boosting, which often outperforms linear regression, Random Forests, and CART.
- Learn how to Develop and Deploy a Gradient Boosting Machine Model - Jan 20, 2017.
GBM is one the hottest machine learning methods. Learn how to create GBM using SciKit-Learn and Python and
understand the steps required to transform features, train, and deploy a GBM.
- Webinar: Modern Regression Modeling for Voter MicroTargeting, Sep 14, Sep 21 - Sep 7, 2016.
Join us for a special 2-part webinar about voting trends, and we will show how machine learning models and data science can be used in elections.
- New Salford Predictive Modeler 8 - Mar 1, 2016.
Salford Predictive Modeler software suite: Faster. More Comprehensive Machine Learning. More Automation. Better results. Take a giant step forward in your data science productivity with SPM 8. Download and try it today!
- Jan 27 Webinar: 3 Ways to Improve your Regression, Part 2 - Jan 26, 2016.
How to take data science techniques even further to extract actionable insight and take advantage of advanced modeling features. You will walk away with several different methods to turn your ordinary regression into an extraordinary regression!
- 3 Ways to Improve your Regression, Jan 20 & 27 Webinars, Hands-on - Jan 12, 2016.
Instead of proceeding with a mediocre analysis, join us for this 2-part webinar series. We will show you how modern algorithms can take your regression model to the next level and expertly handle your modeling woes
- The Present and the Future of the KDD Cup Competition - Aug 31, 2015.
KDD cup is the first and among most prestigious competitions in data science, Among key takeaways from KDD Cup 2015: XGBoost – Gradient Boosted Decision Trees package works wonders in data classification, feature engineering is the king, and team work is crucial.
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- Webinar: Tips & Tricks to Improve Your Logistic Regression, June 25 - Jun 10, 2015.
Learn more advanced and intuitive machine learning techniques that improve on standard logistic regression in accuracy and other aspects. A step-by-step presentation that you can repeat on your own.
- Data Mining for Beginners Boot Camp, Salford video series - Jan 29, 2014.
This series shows how to easily apply SPM software suite to your predictive modeling projects, using a modern banking application as an example. This series is at the beginner level, and is perfect for first-time users or for those who need a refresher course in model building and data analysis.