<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Vivek&apos;s Weblog</title><description>R, Python, Haskell, and Julia code examples that run inline in the browser.</description><link>https://vivekraj.online/</link><item><title>Beyond Accuracy: Classification Metrics and Class Imbalance in MLJ.jl</title><link>https://vivekraj.online/blog/classification-metrics-mlj/</link><guid isPermaLink="true">https://vivekraj.online/blog/classification-metrics-mlj/</guid><description>Why accuracy lies on imbalanced data, and how precision, recall, F1, ROC-AUC, and precision-recall curves tell the real story — built up from the math and implemented in Julia with MLJ.jl, on a synthetic churn dataset with a known, verified answer.</description><pubDate>Sat, 12 Sep 2026 00:00:00 GMT</pubDate><category>julia</category><category>mlj</category><category>machine-learning</category><category>classification</category><category>statistics</category><author>Vivek Raj</author></item><item><title>A Practical Guide to Sampling Methods, in R and Julia</title><link>https://vivekraj.online/blog/sampling-methods-r-julia/</link><guid isPermaLink="true">https://vivekraj.online/blog/sampling-methods-r-julia/</guid><description>Simple random, systematic, stratified, and cluster sampling, plus the bootstrap — explained with the math, implemented in both R and Julia, and checked with a Monte Carlo simulation that shows exactly why stratified sampling wins and cluster sampling doesn&apos;t.</description><pubDate>Thu, 03 Sep 2026 00:00:00 GMT</pubDate><category>statistics</category><category>r</category><category>julia</category><category>data-science</category><category>sampling</category><author>Vivek Raj</author></item><item><title>A/B Testing and Statistical Significance, Explained With a Simulation</title><link>https://vivekraj.online/blog/ab-testing-statistical-significance/</link><guid isPermaLink="true">https://vivekraj.online/blog/ab-testing-statistical-significance/</guid><description>What a p-value actually means, why &apos;statistically significant&apos; isn&apos;t the same as &apos;real,&apos; and why peeking at your A/B test results early quietly wrecks your false-positive rate — demonstrated with a runnable Monte Carlo simulation, not just formulas.</description><pubDate>Fri, 28 Aug 2026 22:00:00 GMT</pubDate><category>statistics</category><category>python</category><category>data-science</category><category>a-b-testing</category><category>experimentation</category><author>Vivek Raj</author></item><item><title>Demystifying PCA: The Math Behind Dimensionality Reduction, Implemented from Scratch</title><link>https://vivekraj.online/blog/pca-dimensionality-reduction/</link><guid isPermaLink="true">https://vivekraj.online/blog/pca-dimensionality-reduction/</guid><description>How Principal Component Analysis actually works — covariance matrices, eigenvectors, and explained variance — built from scratch in NumPy, cross-checked against scikit-learn, and visualized with Bokeh.</description><pubDate>Fri, 28 Aug 2026 18:00:00 GMT</pubDate><category>machine-learning</category><category>python</category><category>data-science</category><category>statistics</category><category>dimensionality-reduction</category><author>Vivek Raj</author></item><item><title>MLJ.jl vs Scikit-Learn: A Deep Dive Into Julia&apos;s Machine Learning Toolbox</title><link>https://vivekraj.online/blog/mlj-vs-scikit-learn/</link><guid isPermaLink="true">https://vivekraj.online/blog/mlj-vs-scikit-learn/</guid><description>A comprehensive guide to MLJ.jl — Julia&apos;s unified machine learning framework — with worked examples, a feature-by-feature comparison against scikit-learn, and an honest look at where each one wins.</description><pubDate>Fri, 28 Aug 2026 00:00:00 GMT</pubDate><category>julia</category><category>mlj</category><category>machine-learning</category><category>scikit-learn</category><category>data-science</category><author>Vivek</author></item><item><title>The Complete Guide to Julia DataFrames.jl</title><link>https://vivekraj.online/blog/julia-dataframes-guide/</link><guid isPermaLink="true">https://vivekraj.online/blog/julia-dataframes-guide/</guid><description>A comprehensive, example-driven tour of DataFrames.jl — every core feature explained and benchmarked against pandas, NumPy, and R, so you know exactly when Julia&apos;s data tooling pulls ahead.</description><pubDate>Wed, 26 Aug 2026 00:00:00 GMT</pubDate><category>julia</category><category>dataframes</category><category>pandas</category><category>numpy</category><category>r</category><category>data-science</category><category>performance</category><author>Vivek Raj</author></item><item><title>Demystifying Logistic Regression: From Mathematics to Production-Ready Python</title><link>https://vivekraj.online/blog/logistic-regression/</link><guid isPermaLink="true">https://vivekraj.online/blog/logistic-regression/</guid><description>A comprehensive guide to Logistic Regression covering math fundamentals, loss functions, regularization (L1/L2), and a complete, interactively-plotted Python implementation with NumPy, Pandas, Scikit-Learn, and Bokeh.</description><pubDate>Wed, 26 Aug 2026 00:00:00 GMT</pubDate><category>machine-learning</category><category>python</category><category>data-science</category><category>scikit-learn</category><category>algorithms</category><author>AI &amp; Data Science Team</author></item></channel></rss>