Press "Enter" to skip to content

Category: Data Science

A Primer on Outlier Detection

Published 2024-10-31 by Kevin Feasel

Jayita Gulati provides an overview:

Anomaly detection means finding patterns in data that are different from normal. These unusual patterns are called anomalies or outliers. In large datasets, finding anomalies is harder. The data is big, and patterns can be complex. Regular methods may not work well because there is so much data to look through. Special techniques are needed to find these rare patterns quickly and easily. These methods help in many areas, like banking, healthcare, and security.

Let’s have a concise look at anomaly detection techniques for use on large scale datasets. This will be no-frills, and be straight to the point in order for you to follow up with additional materials where you see fit.

Outlier detection is a large an interesting space. I suppose I should shill for myself a little bit and note that I wrote a book on the topic. This post provides some quick guidance around outlier detection techniques and applications, and serves as a fine starting point for digging in further.

Comments closed

Monitoring R Models in Production with Vetiver

Published 2024-10-31 by Kevin Feasel

Myles Mitchell continues a series on Vetiver:

In those blogs, we introduced the {vetiver} package and its use as a tool for streamlined MLOps. Using the {palmerpenguins} dataset as an example, we outlined the steps of training a model using {tidymodels} then converting this into a {vetiver} model. We then demonstrated the steps of versioning our trained model and deploying it into production.

Getting your first model into production is great! But it’s really only the beginning, as you will now have to carefully monitor it over time to ensure that it continues to perform as expected on the latest data. Thankfully, {vetiver} comes with a suite of functions for this exact purpose!

Click through for the full story.

Comments closed

A Survey of Predictive Analytics Techniques

Published 2024-10-25 by Kevin Feasel

Akmal Chaudhri tries a bunch of things:

In this short article, we’ll explore loan approvals using a variety of tools and techniques. We’ll begin by analyzing loan data and applying Logistic Regression to predict loan outcomes. Building on this, we’ll integrate BERT for Natural Language Processing to enhance prediction accuracy. To interpret the predictions, we’ll use SHAP and LIME explanation frameworks, providing insights into feature importance and model behavior. Finally, we’ll explore the potential of Natural Language Processing through LangChain to automate loan predictions, using the power of conversational AI.

Click through for the notebook, as well as an overview of what the notebook includes. I don’t particularly like word clouds as the “solution” in the BERT example, though without real data to perform any sort of NLP, there’s not much you can meaningfully do.

Comments closed

RandomWalker 0.2.0 Release

Published 2024-10-24 by Kevin Feasel

Steven Sanderson makes an announcement:

In the ever-evolving landscape of R programming, packages continually refine their capabilities to meet the growing demands of data analysts and researchers. Today, we’re excited to announce the release of RandomWalker version 0.2.0, a minor update that brings significant enhancements to time series analysis and random walk simulations.

RandomWalker has been a go-to package for R users in finance, economics, and other fields dealing with time-dependent data. This latest release introduces new functions and improvements that promise to streamline workflows and provide deeper insights into time series data.

Read on to see what has changed.

Comments closed

Supply Chain Analysis in R via planr

Published 2024-10-22 by Kevin Feasel

Matt Dancho shows off an R package:

Supply chain management is all about balancing supply and demand to ensure that inventory levels are optimized. Overestimating demand leads to excess stock, while underestimating it causes shortages. Accurate inventory projections allow businesses to plan ahead, make data-driven decisions, and avoid costly errors like over-buying inventory or getting into a stock-outage and having no inventory to meet demand.

Read on to learn more about the package and how it works. H/T R-Bloggers.

Comments closed

Smoothing Functions in R

Published 2024-10-11 by Kevin Feasel

Ivan Svetunkov puts on the forecasting hat:

I have been asked recently by a colleague of mine how to extract the variance from a model estimated using adam() function from the smooth package in R. The problem was that that person started reading the source code of the forecast.adam() and got lost between the lines (this happens to me as well sometimes). Well, there is an easier solution, and in this post I want to summarise several methods that I have implemented in the smooth package for forecasting functions. In this post I will focus on the adam() function, although all of them work for es() and msarima() as well, and some of them work for other functions (at least as for now, for smooth v4.1.0). Also, some of them are mentioned in the Cheat sheet for adam() function of my monograph (available online).

Read on to learn more. H/T R-Bloggers.

Comments closed

An Overview of k Nearest Neighbors

Published 2024-10-07 by Kevin Feasel

Harris Amjad explains a common algorithm for classification:

It so happens that given the hype of Machine Learning (ML) and especially Large Language Models these days, there is a considerable proportion of those who wish to understand how these systems work from scratch. Unfortunately, more often than not, the interest fades away quickly as learners jump to complicated algorithms like neural networks and transformers first, without giving heed to traditional ML algorithms that paved the foundation for these advanced algorithms in the first place. In this tip, we will introduce and implement the K-Nearest Neighbors model in Python. Although it is quite old, it remains very popular due to its simplicity and intuitiveness.

Click through to learn more about this algorithm, including an implementation from scratch in Python.

Comments closed

An Overview of LightGBM

Published 2024-10-03 by Kevin Feasel

Vinod Chugani continues a series on tree-based classification techniques:

LightGBM is a highly efficient gradient boosting framework. It has gained traction for its speed and performance, particularly with large and complex datasets. Developed by Microsoft, this powerful algorithm is known for its unique ability to handle large volumes of data with significant ease compared to traditional methods.

In this post, we will experiment with LightGBM framework on the Ames Housing dataset. In particular, we will shed some light on its versatile boosting strategies—Gradient Boosting Decision Tree (GBDT) and Gradient-based One-Side Sampling (GOSS). These strategies offer distinct advantages. Through this post, we will compare their performance and characteristics.

Read on to learn more about LightGBM as an algorithm, as well as how to use it.

Comments closed

Plotting the ROC Curve in Microsoft Fabric

Published 2024-10-03 by Kevin Feasel

Tomaz Kastrun gets plotting:

ROC (Receiver Operation Characteristics) – curve is a graph that shows how classifiers performs by plotting the true positive and false positive rates. It is used to evaluate the performance of binary classification models by illustrating the trade-off between True positive rate (TPR) and False positive rate (FPR) at various threshold settings.

Read on to see how you can generate one in a Microsoft Fabric notebook. Tomaz also plots a density function for additional fun.

Comments closed

Handling Missing Data with XGBoost

Published 2024-09-30 by Kevin Feasel

Vinod Chugani is missing a few data points:

XGBoost has gained widespread recognition for its impressive performance in numerous Kaggle competitions, making it a favored choice for tackling complex machine learning challenges. Known for its efficiency in handling large datasets, this powerful algorithm stands out for its practicality and effectiveness.

In this post, we will apply XGBoost to the Ames Housing dataset to demonstrate its unique capabilities. Building on our prior discussion of the Gradient Boosting Regressor (GBR), we will explore key features that differentiate XGBoost from GBR, including its advanced approach to managing missing values and categorical data.

Read on to see how it fares.

Comments closed