Blog Posts – R’tichoke

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Multi-Task Learning with torch in R

R

Deep Learning

torch

Multi-Task Learning

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

Building a Simple Neural Network in R with torch

R

Deep Learning

torch

The torch package brings the power of deep learning to R by providing bindings to the popular PyTorch library. In this post, you’ll learn how to build and train a simple…

Understanding Variability in Credit Score Predictions

R

Credit Risk Analytics

Bootstrapping

Credit scoring models work well in the middle of the score distribution but often become less reliable at the extremes where data is sparse. This post shows how to use…

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…

Using Bayesian Optimization to Tune XGBoost Models in R

R

Analytics

Machine Learning

Tuning machine learning models can be time-consuming and computationally expensive. This post shows how to use Bayesian optimization to efficiently find optimal XGBoost…

Building a Particle Swarm Optimizer from Scratch in R

R

Optimization

Visualization

Nature-inspired algorithms can solve complex optimization problems with surprising efficiency. This post shows you how to build a Particle Swarm Optimizer (PSO) from scratch…

Custom Charting Functions Using ggplot2

R

Data Visualization

ggplot2

While R has a variety of options for 2D graphics and data visualization, it’s hard to beat ggplot2 in terms of features, functionality, and overall visual quality. This post…

Generating Correlated Random Numbers in R from Scratch

R

Statistics

Simulation

Need random data with specific correlation patterns for your simulations? This post shows you how to generate correlated random numbers in R using a simple matrix approach –…

Measuring Model Performance Using a Gains Table

R

Credit Risk Analytics

Model Evaluation

In credit risk modeling, analysts often use a tool called a gains table (or KS table) to measure and quantify the performance of classification models. This post explores…

Portfolio Optimization Using Particle Swarm Optimization in R

R

Finance

Optimization

Portfolio optimization is a critical task in investment management, where the goal is to allocate capital across different assets to maximize returns while controlling risk.…

Introduction to R for Analytics

R

Analytics

Introduction

R is a powerful language specifically designed for data analysis and visualization. This guide will walk you through practical examples of using R for real-world analytics…

Monotonic Binning Using XGBoost

R

Credit Risk Analytics

XGBoost

Monotonic binning is a technique where variable values are grouped into bins such that event rates increase or decrease consistently across these bins. This approach is…

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…