- The Explainable Boosting Machine - May 13, 2021.
As accurate as gradient boosting, as interpretable as linear regression.
- Gradient Boosted Decision Trees – A Conceptual Explanation - Apr 30, 2021.
Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.
- Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1 - Apr 22, 2021.
New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.
- Beautiful decision tree visualizations with dtreeviz - Mar 8, 2021.
Improve the old way of plotting the decision trees and never go back!
- Telling a Great Data Story: A Visualization Decision Tree - Feb 15, 2021.
Pick your visualizations strategically. They need to tell a story.
- Machine Learning – it’s all about assumptions - Feb 11, 2021.
Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.
- All Machine Learning Algorithms You Should Know in 2021 - Jan 4, 2021.
Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.
- How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
- 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.
- Visualizing Decision Trees with Python (Scikit-learn, Graphviz, Matplotlib) - Apr 15, 2020.
Learn about how to visualize decision trees using matplotlib and Graphviz.
- KDnuggets™ News 20:n09, Mar 4: When Will AutoML replace Data Scientists (if ever) – vote; 20 AI, DS, ML Terms You Need to Know (part 2) - Mar 4, 2020.
- Decision Tree Intuition: From Concept to Application - Feb 27, 2020.
While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.
- Handling Trees in Data Science Algorithmic Interview - Jan 16, 2020.
This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview.
- Decision Tree Algorithm, Explained - Jan 14, 2020.
All you need to know about decision trees and how to build and optimize decision tree classifier.
- Common Machine Learning Obstacles - Sep 9, 2019.
In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.
- Comparing Decision Tree Algorithms: Random Forest® vs. XGBoost - Aug 21, 2019.
Check out this tutorial walking you through a comparison of XGBoost and Random Forest. You'll learn how to create a decision tree, how to do tree bagging, and how to do tree boosting.
- Understanding Decision Trees for Classification in Python - Aug 21, 2019.
This tutorial covers decision trees for classification also known as classification trees, including the anatomy of classification trees, how classification trees make predictions, using scikit-learn to make classification trees, and hyperparameter tuning.
- 3 Main Approaches to Machine Learning Models - Jun 11, 2019.
Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.
- Random Forests® vs Neural Networks: Which is Better, and When? - Jun 7, 2019.
Random Forests and Neural Network are the two widely used machine learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?
- 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.
- Decision Trees — An Intuitive Introduction - Feb 14, 2019.
An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.
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- 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.
- Random forests® explained intuitively - Jan 30, 2019.
A detailed explanation of random forests, with real life use cases, a discussion into when a random forest is a poor choice relative to other algorithms, and looking at some of the advantages of using random forest.
- KDnuggets™ News 19:n01, Jan 3: The Essence of Machine Learning; A Guide to Decision Trees for Machine Learning and Data Science - Jan 3, 2019.
Also: 10 More Must-See Free Courses for Machine Learning and Data Science; Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning; Feature engineering, Explained; Papers with Code: A Fantastic GitHub Resource for Machine Learning; BERT: State of the Art NLP Model, Explained
- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
- A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
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- 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.
- Introduction to Python Ensembles - Feb 9, 2018.
In this post, we'll take you through the basics of ensembles — what they are and why they work so well — and provide a hands-on tutorial for building basic ensembles.
Pages: 1 2
- 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.
Pages: 1 2
- Random Forests®, Explained - Oct 17, 2017.
Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.
- Top KDnuggets tweets, Oct 04-10: Using #MachineLearning to Predict, Explain Attrition; Tidyverse, an opinionated #DataScience Toolbox in R - Oct 11, 2017.
Also #MachineLearning: Understanding Decision Tree Learning; #PyTorch tutorial distilled - Moving from #TensorFlow to PyTorch.
- Lessons Learned From Benchmarking Fast Machine Learning Algorithms - Aug 16, 2017.
Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.
- The Machine Learning Abstracts: Decision Trees - Aug 3, 2017.
Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.
- Simplifying Decision Tree Interpretability with Python & Scikit-learn - May 19, 2017.
This post will look at a few different ways of attempting to simplify decision tree representation and, ultimately, interpretability. All code is in Python, with Scikit-learn being used for the decision tree modeling.
- 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.
- 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.
- Decision Tree Classifiers: A Concise Technical Overview - Nov 3, 2016.
The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
- KDnuggets™ News 16:n34, Sep 21: The Great Algorithm Tutorial Roundup; 7 Steps to Mastering Apache Spark 2.0 - Sep 21, 2016.
The Great Algorithm Tutorial Roundup; 7 Steps to Mastering Apache Spark 2.0; Machine Learning in a Year: From Total Noob to Effective Practitioner; Learning From Data (Introductory Machine Learning) Caltech MOOC
- The Great Algorithm Tutorial Roundup - Sep 20, 2016.
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!
- Decision Trees: A Disastrous Tutorial - Sep 15, 2016.
Get a concise overview of decision trees here, one of the most used KDnuggets reader algorithms as measured in a recent poll.
- Top Algorithms and Methods Used by Data Scientists - Sep 12, 2016.
Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.
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- Machine Learning Classic: Parsimonious Binary Classification Trees - Jun 14, 2016.
Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.
- Dealing with Unbalanced Classes, SVMs, Random Forests®, and Decision Trees in Python - Apr 29, 2016.
An overview of dealing with unbalanced classes, and implementing SVMs, Random Forests, and Decision Trees in Python.
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- Salford Predictive Modeler 8: Faster. More Machine Learning. Better results - Apr 4, 2016.
Take a giant step forward with SPM 8: Download and try it for yourself just released version 8 and get better results.
- 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!
- Tree Kernels: Quantifying Similarity Among Tree-Structured Data - Feb 23, 2016.
An in-depth, informative overview of tree kernels, both theoretical and practical. Includes a use case and some code after the discussion.
Pages: 1 2 3
- Top 10 tweets Jan 25-31: DataViz: how a decision tree works; Nice and Brief Tutorial on Python - Feb 1, 2016.
DataViz - how a decision tree makes classifications; Very Nice and Brief Tutorial on #Python #DataScience #DataViz; Per Einstein, time flows slower in Meetings than in empty space #hum; Top 10 Skills for #DataScience professionals.
- Hitchhikers Guide to Azure Machine Learning Studio - Jan 15, 2016.
Learn Azure ML Studio through this brief hands-on tutorial. This step-by-step guide will help you get a quick-start and grasp the basics of this Predictive Modeling tool.
Pages: 1 2 3 4
- All Machine Learning Models Have Flaws - Mar 3, 2015.
This classic post examines what is right and wrong with different models of machine learning, including Bayesian learning, Graphical Models, Convex Loss Optimization, Statistical Learning, and more.
- Top KDnuggets tweets, Jan 26 – Feb 1: Good list of Machine Learning Resources; Sample Machine Learning solutions with R - Feb 2, 2015.
Good list of #MachineLearning Resources, #DeepLearning, Graphical Models; Sample #MachineLearning solutions with R on #Azure ML Marketplace #rstats; Decision Tree Algorithms: comparing Gini Index, Chi-Square, Information Gain; Cartoon: Lets solve 2+4=? first, worry about #DataMining Later.
- Data Analytics for Business Leaders Explained - Sep 22, 2014.
Learn about a variety of different approaches to data analytics and their advantages and limitations from a business leader's perspective in part 1 of this post on data analytics techniques.
- 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.
- Webinar, Apr 3: Best Decision Trees just got better with Angoss KnowledgeSEEKER 9.0 - Mar 23, 2014.
This Apr 3 webinar will show how KnowledgeSEEKER 9.0 will make your modeling faster with automated workflow for building, refreshing, and reusing workflows - all with the click of a button.
- Webinar: Best Decision Trees just got better with Angoss KnowledgeSEEKER 9.0, Apr 3 - Mar 14, 2014.
New KnowledgeSEEKER 9.0 makes your modeling faster with automated workflow for building, refreshing, and reusing workflows - all with the click of a button. Learn more on Apr 3.
- Introduction to Random Forests® for Beginners – free ebook - Mar 6, 2014.
Random Forests is of the most powerful and successful machine learning techniques. This free ebook will help beginners to leverage the power of Random Forests.
- 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.