https://www.michaelrlandon.com/blog daily 0.75 2025-10-25 https://www.michaelrlandon.com/blog/why-sam-altman-and-elon-musk-are-wrong-and-ontology-is-the-next-step-in-ai monthly 0.5 2024-10-17 https://www.michaelrlandon.com/blog/from-feeds-to-discovery monthly 0.5 2024-05-04 https://www.michaelrlandon.com/blog/lets-talk-about-reality-and-what-it-means-for-agi monthly 0.5 2023-06-20 https://www.michaelrlandon.com/blog/south-bay-la-the-unassuming-powerhouse-of-global-culture monthly 0.5 2023-06-12 https://www.michaelrlandon.com/blog/the-slipping-horizon-navigating-the-volatile-seas-of-post-reality-markets monthly 0.5 2023-06-06 https://www.michaelrlandon.com/blog/the-bias-variance-tradeoff monthly 0.5 2022-10-13 https://images.squarespace-cdn.com/content/v1/6021d2078af2e31d40574518/7f45a9af-f25c-4307-b616-e1097abbfa34/bullseye.png Blog - The Bias-Variance Tradeoff - Figure 1 - Bias & Variance shown by data points on target Figure 1. Bias and Variance illustrated as results on a target Source: https://scott.fortmann-roe.com/docs/BiasVariance.html https://images.squarespace-cdn.com/content/v1/6021d2078af2e31d40574518/a65ccd80-f305-4af2-8cf4-b47c1d71bfb8/1_9hPX9pAO3jqLrzt0IE3JzA.png Blog - The Bias-Variance Tradeoff - Make it stand out Figure 2 - Representations of various ML models trained to the same data. Image source: https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229 https://images.squarespace-cdn.com/content/v1/6021d2078af2e31d40574518/a12bc328-7f5a-4612-8ce1-61e2e26dba6e/biasvariance.png Blog - The Bias-Variance Tradeoff - Figure 4 - Graphing Bias^2, Variance, and their sum (plus Noise), Total Error, as a function of model complexity in terms of error. As we have seen above, an overfit model responds to the variation in the data with a complex curve that in the end fails to predict well for new data, whereas an underfit model’s simple curve does little to capture the data variation at all. We want to find some medium point of model complexity that avoids these extremes. So, as Figure 4 shows, we find the optimal model complexity where we minimize the total error, being the lowest possible sum of Variance + Bias^2 + Noise. Image source: https://scott.fortmann-roe.com/docs/BiasVariance.html https://images.squarespace-cdn.com/content/v1/6021d2078af2e31d40574518/f227d48b-26d3-41dc-9554-f938b4c501bd/1.png Blog - The Bias-Variance Tradeoff - Make it stand out Figure 3 - The Tell. Are we in Regime #1 or Regime #2? Image source: https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote12.html https://www.michaelrlandon.com/home daily 1.0 2026-03-24 https://www.michaelrlandon.com/new-page daily 0.75 2025-04-23