posts – R'tichoke

Multi-Task Learning with torch in R

R

Deep Learning

torch

Multi-Task Learning

Multi-task learning (MTL) is an approach where a single neural network model is trained to perform multiple related tasks simultaneously. This methodology can improve model…

Building a Simple Neural Network in R with torch

R

Deep Learning

torch

The torch package brings deep learning to R by providing bindings to the popular PyTorch library. This comprehensive tutorial demonstrates how to build and train a simple…

Tutorial: Assessing Credit Score Prediction Reliability Using Bootstrap Resampling

R

Credit Risk Analytics

Bootstrapping

Credit scoring models demonstrate optimal performance within the central regions of the score distribution, yet exhibit diminished reliability at the distribution extremes…

Building Models in R with tidymodels

R

Machine Learning

tidymodels

The tidymodels framework provides a cohesive set of packages for modeling and machine learning in R, following tidyverse principles. In this post, we’ll build a realistic…

Optimizing XGBoost Hyperparameters Using Bayesian Optimization in R

R

Analytics

Machine Learning

Hyperparameter tuning for machine learning models represents a computationally intensive and time-consuming process. This tutorial demonstrates the implementation of…

Implementing Particle Swarm Optimization from Scratch in R

R

Optimization

Visualization

Nature-inspired optimization algorithms demonstrate remarkable efficiency in solving complex optimization problems. This post provides a implementation of Particle Swarm…

Developing Custom Charting Functions with ggplot2

R

Data Visualization

ggplot2

While R provides numerous options for two-dimensional graphics and data visualization, ggplot2 offers great functionality, features, and visual quality. This tutorial shows…

Generating Correlated Random Numbers in R Using Matrix Methods

R

Statistics

Simulation

The generation of random data with specified correlation patterns can be useful in statistical simulation and this tutorial provides a methodology for creating correlated…

Evaluating Binary Classification Models Using Gains Tables

R

Credit Risk Analytics

Model Evaluation

In credit risk modeling and binary classification applications, analysts employ gains tables (also known as KS tables) as a fundamental tool for measuring and quantifying…

Portfolio Optimization Using PSO

R

Finance

Optimization

Portfolio optimization represents a critical task in investment management, where the goal involves allocating capital across different assets to maximize returns while…

Introduction to R for Analytics

R

Analytics

Introduction

R is a powerful language specifically designed for data analysis and visualization. This guide demonstrates practical examples of using R for real-world analytics tasks.

Monotonic Binning Using XGBoost

R

Credit Risk Analytics

XGBoost

This post focuses on how to implement monotonic binning, a method that groups variable values into bins where event rates demonstrate consistent monotonic behavior. This…

Getting Started with Python using R and Reticulate

R

Python

reticulate

Want to use Python’s powerful libraries without leaving R? The reticulate package gives you the best of both worlds - R’s elegant data handling and visualization with…