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.
Automated Machine Learning (AutoML) effortlessly applies ML algorithms to PostgreSQL data for insights and predictions. Using mljar-supervised, an open-source Python package, this guide simplifies the process with code snippets.
MLJAR, an open-source AutoML library, transformed my machine learning journey. Its user-friendly interface automates tasks, making it a top performer in Hackathons. Experience efficiency without extensive coding.
To compute and visualize feature importance with Xgboost in Python, the tutorial covers built-in Xgboost feature importance, permutation method, and SHAP values.
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.
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.
A Decision Tree is a supervised machine learning algorithm used for classification and regression. This article demonstrates four ways to visualize Decision Trees in Python, including text representation, plot_tree, export_graphviz, and dtreeviz.