# Tag: Random Forests (39)

**Intro to XGBoost: Predicting Life Expectancy with Supervised Learning**- May 8, 2019.

Today we’ll use XGBoost Boosted Trees for regression over the official Human Development Index dataset. XGBoost is a framework that allows us to train Boosted Trees exploiting multicore parallelism.**KDnuggets™ News 19:n13, Apr 3: Top 10 Data Scientist Coding Mistakes; Explaining Random Forest; Which Face is Real?**- Apr 3, 2019.

Do you know when is using "for" loop a mistake? Read 10 top coding mistakes by Data Scientists; Understand Random Forests and Linear Regression with scikit-learn; Find how to choose the right chart type; and see if you can guess which face is real.**Explaining Random Forest (with Python Implementation)**- Mar 29, 2019.

We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation.**KDnuggets™ News 19:n06, Feb 6: Data Scientists: Why are they so expensive to hire? An Essential Data Science Venn Diagram**- Feb 6, 2019.

Also an overview of main methods for Dimension Reduction; an intuitive explanation of Random Forests; how to avoid data visualization disasters; and trending Deep Learning Github repos.**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.**Data Scientist Interviews Demystified**- Aug 2, 2018.

We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.**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.**Top Data Science and Machine Learning Methods Used in 2017**- Dec 11, 2017.

The most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests; Deep Learning is used by only 20% of respondents; we also analyze which methods are most "industrial" and most "academic".**KDnuggets™ News 17:n40, Oct 18: Want to Become a Data Scientist? Read This!; Natural Stupidity is more Dangerous than Artificial Intelligence**- Oct 18, 2017.

Want to Become a Data Scientist? Read This Interview First; Natural Stupidity is more Dangerous than Artificial Intelligence; Random Forests(r), Explained; Key Trends and Takeaways from RE-WORK Deep Learning Summit Montreal; An Overview of 3 Popular Courses on Deep Learning**Random Forests(r), 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.**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.**Learn How to Make Machine Learning Work (webinars every Tue in October, Live or on-demand)**- Sep 28, 2017.

To fully use machine learning, we first need to understand both the potential benefits and the techniques to create data-driven models. In this webinar series, we will show you how to easily and automatically apply complex algorithms to data in real world applications.**Webinar: Improve Your CLASSIFICATION with CART(r) and RandomForests(r), Mar 29**- Mar 27, 2017.

We discuss the advantages of tree based techniques, including automatic variable selection, variable interactions, nonlinear relationships, outliers, and missing values.**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.**Improve Your Regression with CART and RandomForests, Jan 26 Webinar**- Jan 23, 2017.

Learn the fundamentals of tree-based machine learning algorithms and how to easily fine tune and improve your Random Forest regression models.**KDnuggets™ News 16:n43, Dec 7: Where did you use Data Science? The hard thing about Deep Learning; Big Data Main Events in 2016, Key Trends for 2017**- Dec 7, 2016.

Where did you apply Analytics, Data Science in 2016? Big Data Main Developments in 2016 and Key Trends in 2017; The Data Science Delusion; The hard thing about deep learning.**Random Forests in Python**- Dec 2, 2016.

Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.**The Evolution of Classification, Oct 19, Oct 26 Webinars**- Oct 7, 2016.

Join us for this two part webinar series on the Evolution of Classification, presented by Senior Scientist, Mikhail Golovnya.**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!**Random Forest: A Criminal Tutorial**- Sep 19, 2016.

Get an overview of Random Forest here, one of the most used algorithms by KDnuggets readers according to a recent poll.**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.**Improve Your Regression with Modern Regression Analysis Techniques, July 27, Aug 10 Webinars**- Jul 22, 2016.

This two part webinar will help you improve your regression using modern regression analysis techniques. July 27 (part 1) and August 10 (part 2).**KDnuggets™ News 16:n15, Apr 27: Deep Learning vs. SVMs, Random Forests; Python Guide for Data Science**- Apr 27, 2016.

When Does Deep Learning Work Better Than SVMs or Random Forests; Comprehensive Guide to Learning Python for Data Science; Top 10 IPython Notebook Tutorials for Data Science and Machine Learning; 5,000 KDnuggets Posts - Examining Our Most Popular Content**When Does Deep Learning Work Better Than SVMs or Random Forests?**- Apr 22, 2016.

Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.**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**Topological Analysis and Machine Learning: Friends or Enemies?**- Sep 29, 2015.

What is the interaction between Topological Data Analysis and Machine Learning ? A case study shows how TDA decomposition of the data space provides useful features for improving Machine Learning results.**Top 10 Quora Machine Learning Writers and Their Best Advice**- Sep 18, 2015.

Top Quora machine learning writers give their advice on pursuing a career in the field, academic research, and selecting and using appropriate technologies.**Top KDnuggets tweets, Aug 25-31: How to become a #DataScientist for Free; The R universe of Hadley Wickham**- Sep 1, 2015.

How to become a Data Scientist for Free; #BigData is Out, #MachineLearning is in; The universe of Hadley Wickham, the Man Who Revolutionized R; Book review: Fundamentals of #DeepLearning.**Top 10 R Packages to be a Kaggle Champion**- Apr 21, 2015.

Kaggle top ranker Xavier Conort shares insights on the “10 R Packages to Win Kaggle Competitions”.**Machine Learning 201: Does Balancing Classes Improve Classifier Performance?**- Apr 9, 2015.

The author investigates if balancing classes improves performance for logistic regression, SVM, and Random Forests, and finds where it helps the performance and where it does not.**Webinar: 3 Ways to Improve your Regression, Feb 10**- Jan 20, 2015.

We will show how MARS regression, TreeNet gradient boosting, and Random Forests can take your regression model to the next level with modern algorithms.**Top KDnuggets tweets, Dec 7-14: Google new CAPTCHA trains #AI; Random Forests, SVM give best results**- Dec 15, 2014.

Which one is the bunny? Google new CAPTCHA bot-trap trains #AI; O'Reilly Data Scientist Salary and Tools Survey 2014; Microsoft brings the power of #MachineLearning to Office Online; 10 Data Science Newsletters to Subscribe to.**Comprehensive Data Science Training by Salford Systems, Dec 3-5, Online or San Diego**- Nov 4, 2014.

Learn the basics tree-structured data mining with CART, and progress to more advanced topics including Linear, Logistic, Nonlinear, Regularized, Lasso, MARS, TreeNet (Stochastic Gradient Boosting) and RandomForests(r), including Latest Refinements and Model Compression.**Salford Comprehensive Data Science Training, Dec 3-5, San Diego or Online**- Oct 21, 2014.

Learn the basics tree-structured data mining with CART, and progress to more advanced topics including Linear, Logistic, Nonlinear, Regularized, Lasso, MARS, TreeNet (Stochastic Gradient Boosting) and RandomForests(r), including Latest Refinements and Model Compression.**Top KDnuggets tweets, Jul 18-20: Baby steps in Learning Python; 7 Steps for Learning Data Mining**- Jul 21, 2014.

Baby steps in learning #Python for data analysis; My 7 Steps for Learning Data Mining and Data Science - now in Techopedia; A good collection of #MachineLearning tools in #Python; Understanding Random Forests: From Theory to Practice - implementation.**Top stories for Mar 2-8: Do’s and Don’t of Data Mining; Wolfram Breakthtough language**- Mar 9, 2014.

The Dos and Donts of Data Mining; Wolfram Breakthrough Knowledge-based Programming Language - what it means for Data Science? Introduction to Random Forests for Beginners - free ebook; Exclusive Interview with Quentin Clark, Microsoft Data Platform Group.**Top KDnuggets tweets, Mar 5-6: Data Science Backlash begins; Intro to Random Forests for Beginners – free ebook**- Mar 7, 2014.

Backlash begins: Data Science is not a science, and not a good job prospect; Intro to Random Forests for Beginners - free ebook; Must read for data scientists: Q - new Data Language; Book: R for Business Analytics.**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.