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Decision Trees
Modeling Decision Trees

Modeling Decision Trees

Machine LearningModelingDecision Treesposted by Diego Lopez Yse June 30, 2020

Decision trees (DTs) are one of the most popular algorithms in machine learning: they are easy to visualize, highly interpretable, super flexible, and can be applied to both classification and regression problems. DTs predict the value of a target variable by learning simple decision rules inferred... Read more
How To Build A Spam Classifier Using Decision Tree

How To Build A Spam Classifier Using Decision Tree

Machine LearningModelingDecision Treesspam classifierposted by Leihua Ye February 25, 2020

In the realm of Supervised Learning, there are tons of classifiers, including Logistic Regressions (logit 101 and logit 102), LDA, Naive Bayes, SVM, KNN, Random Forest, Neural Networks, and so many more coming each day! The real question that... Read more
RAPIDS Forest Inference Library: Prediction at 100 Million Rows per Second

RAPIDS Forest Inference Library: Prediction at 100 Million Rows per Second

Machine LearningModelingDecision Treesposted by RAPIDS October 17, 2019

Random forests (RF) and gradient-boosted decision trees (GBDTs) have become workhorse models of applied machine learning. XGBoost and LightGBM, popular packages implementing GBDT models, consistently rank among the most commonly used tools by data scientists on the Kaggle platform. We see similar interest in forest-based models... Read more
The Complete Guide to Decision Trees (part 2)

The Complete Guide to Decision Trees (part 2)

Machine LearningModelingDecision TreesMachine Learningposted by Diego Lopez Yse May 21, 2019

(See part 1 here.) Now you may ask yourself: how do DTs know which features to select and how to split the data? To understand that, we need to get into some details. All DTs perform basically the same task: they examine all the attributes of... Read more
The Complete Guide to Decision Trees (part 1)

The Complete Guide to Decision Trees (part 1)

Guest contributorMachine LearningModelingDecision TreesMachine Learningposted by Diego Lopez Yse May 15, 2019

In the beginning, learning Machine Learning (ML) can be intimidating. Terms like “Gradient Descent”, “Latent Dirichlet Allocation” or “Convolutional Layer” can scare lots of people. But there are friendly ways of getting into the discipline, and I think starting with this guide to decision trees is... Read more
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