Machine Learning Expedition

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Machine Learning Expedition

SHAP values for machine learning model explanation

SHAP values for machine learning model explanation

In recent years, machine learning models have become increasingly complex and opaque. As these models are deployed in real-world settings, there is a growing need to interpret how they arrive at predictions. Model explanation and interpretability methods are essential for debugging models, ensuring fairness, and building trust with users. One

Understanding Diffusion Models: An Overview

Diffusion models are taking the world of AI generation by storm. In this in-depth post, we'll unpack how these models work, their key innovations, and why they are outperforming other generative models like GANs. What is Diffusion? In physics, diffusion is the spread of particles from high to

Using Machine Learning for Fraud Detection

Fraud is a major issue that costs companies billions of dollars each year. Detecting fraud accurately and in real-time is critical for reducing losses from fraudulent activities. Machine learning provides a powerful set of techniques that can be used to build highly accurate fraud detection systems. In this technical writeup,

Visualizing XGBoost Trees

XGBoost is a popular gradient-boosting library for building regression and classification models. The core of XGBoost is an ensemble of decision trees. In this post, we'll look at how to visualize and interpret individual trees from an XGBoost model. Let's start by loading a simple sample

LightGBM Hyperparameter Tuning: A Practical Guide

LightGBM is a popular and effective gradient boosting framework that is widely used for tabular data and competitive machine learning tasks. However, like all machine learning models, LightGBM has several hyperparameters that can significantly impact model performance. Tuning these hyperparameters is essential for building high-quality LightGBM models. In this comprehensive

Image Classification with TensorFlow

Image classification is a fundamental task in computer vision, where the goal is to assign a label or category to an image based on its content. TensorFlow, an open-source machine learning framework developed by Google, provides powerful tools and libraries for building and training image classification models. In this comprehensive

Machine learning and its application to face recognition

Machine learning facial recognition in the security industry is a rapidly growing field that utilizes algorithms and statistical models to identify and verify individuals based on their facial features. The technology has numerous applications in various sectors, including law enforcement, border control, and surveillance. Facial recognition is a biometric modality