Flipboard

JUNE 3, 2025

We employed a total weight of 133,425 from the Demographic and Health Survey using STATA Version 17, MS Excel 2016, and Python 3.10 The Decision Tree model was the best-performing one, with an accuracy of 82% and an AUC of 0.87. for data management.

Machine Learning 65

Machine Learning Machine Learning Decision Trees Python 65

Heartbeat

SEPTEMBER 13, 2023

This guide will buttress explainability in machine learning and AI systems. The explainability concept involves providing insights into the decisions and predictions made by artificial intelligence (AI) systems and machine learning models. What is Explainability?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

How to Learn Machine Learning

DECEMBER 4, 2022

Programming a computer with artificial intelligence (Ai) allows it to make decisions on its own. Numerous techniques, such as but not limited to rule-based systems, decision trees, genetic algorithms, artificial neural networks, and fuzzy logic systems, can be used to do this.

AI 52

AI AI Machine Learning Machine Learning 52

Mlearning.ai

FEBRUARY 15, 2023

Figure 1 Preprocessing Data preprocessing is an essential step in building a Machine Learning model. Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. and Schutze H.,

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

DrivenData Labs

MAY 1, 2025

Solvers used 2016 demographics, economic circumstances, migration, physical limitations, self-reported health, and lifestyle behaviors to predict a composite cognitive function score in 2021. Summary of approach: I used LightGBM decision tree algorithm to predict the difference between test participants scores from different years.

Decision Trees 147

Decision Trees Clustering Algorithm Machine Learning 147