Category: random-forest

The study trained 3,600 Random Forest Classifiers on 72 datasets, revealing that optimal tree numbers depend on dataset size and precision in tuning.

This post illustrates three ways to compute feature importance for the Random Forest algorithm using the scikit-learn package in Python. It covers built-in feature importance, the permutation method, and SHAP values, providing code examples.

This post demonstrates how to visualize a Decision Tree from a Random Forest using a Boston dataset for house price regression in scikit-learn.

When using scikit-learn Random Forest algorithm, memory consumption can be an issue, especially with large datasets. This article provides insights and solutions to reduce memory usage in Random Forest.

Choosing between Random Forest and Neural Network depends on the data type. Random Forest suits tabular data, while Neural Network excels with images, audio, and text data.

In the battle of Random Forest versus AutoML, AutoML superior performance is clear. However, I will highlight other advantages, demonstrating how AutoML excels in handling real-world, messy data.

Initially surprised, I believed any complex ML algorithm, like Random Forest (RF), could overfit. Research led to a statement on Leo Breiman website asserting RF doesnt overfit. Intrigued, I delved into theoretical and practical analysis.