Learn 2 methods to save and load machine learning models in scikit-learn. The first, using the pickle package, is faster but takes more storage. The second, employing joblib, conserves disk space but is slower.
Understanding decision rules extracted from a Decision Tree is crucial for implementing it in different languages or environments. In this post, I will show you three ways to get decision rules.
TensorFlow is a deep learning library for constructing Neural Networks, while Scikit-learn is a machine learning library with pre-built algorithms for various tasks. TensorFlow is suited for deep learning, while Scikit-learn is versatile for tabular data tasks.
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.
This post provides guidance on saving and loading Random Forest models trained with scikit-learn in Python. It also demonstrates how to compress the model for smaller file size using the joblib package.
