6 Predictive Models Every Beginner Data Scientist Should Master - Dec 23, 2021.
Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
Linear Regression, Logistic Regression, Machine Learning, random forests algorithm
- A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know - May 6, 2021.
This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.
CatBoost, Ensemble Methods, Machine Learning, Python, random forests algorithm, scikit-learn, XGBoost
- 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning - Apr 8, 2020.
Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?
Machine Learning, Neural Networks, random forests algorithm
- Random Forest® — A Powerful Ensemble Learning Algorithm - Jan 22, 2020.
The article explains the Random Forest algorithm and how to build and optimize a Random Forest classifier.
Algorithms, Ensemble Methods, Python, random forests algorithm
- Random Forest® vs Neural Networks for Predicting Customer Churn - Dec 26, 2019.
Let us see how random forest competes with neural networks for solving a real world business problem.
Churn, Customer Analytics, Neural Networks, random forests algorithm
- 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.
ActiveState, Decision Trees, Python, random forests algorithm, XGBoost
- Coding Random Forests® in 100 lines of code* - Aug 7, 2019.
There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.
Algorithms, Machine Learning, Multicollinearity, R, random forests algorithm
- XGBoost and Random Forest® with Bayesian Optimisation - Jul 8, 2019.
This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.
Bayesian, Optimization, Python, random forests algorithm, XGBoost
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?
Decision Trees, Neural Networks, random forests algorithm
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.
Explained, Machine Learning, Python, random forests algorithm
- 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.
Decision Trees, Explained, random forests algorithm
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.
Data Science Skills, Hiring, Interview Questions, P-value, random forests algorithm, XGBoost
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.
Algorithms, CART, Decision Trees, Ensemble Methods, Explained, Machine Learning, random forests algorithm, Salford Systems
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.
Algorithms, Ensemble Methods, Gradient Boosting, Machine Learning, Neural Networks, Predictive Analytics, random forests algorithm, SVM
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.
Algorithms, Dataiku, Decision Trees, Gradient Boosting, Linear Regression, Logistic Regression, random forests algorithm
- 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.
Algorithms, Classification, Ensemble Methods, Python, random forests algorithm, Yhat
- 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!
Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization
- 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.
Algobeans, CA, Crime, random forests algorithm, San Francisco
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.
Advice, Deep Learning, random forests algorithm, Support Vector Machines, SVM
- 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.
Ayasdi, Machine Learning, random forests algorithm, Topological Data Analysis
- 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.
Machine Learning, Quora, random forests algorithm, Top 10, Xavier Amatriain, Yoshua Bengio
- 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”.
Kaggle, R Packages, random forests algorithm, Success, SVM, Text Analysis, Xavier Conort
- 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.
Beginners, Decision Trees, ebook, Free, Kaggle, random forests algorithm, Salford Systems
