https://blazskufca.com/tags/machine-learning/Recent content in Machine Learning on Blaž ŠkufcaHugoen-usThu, 02 Apr 2026 00:00:00 +0000https://blazskufca.com/projects/stylometry_with_nb/Thu, 02 Apr 2026 00:00:00 +0000https://blazskufca.com/projects/stylometry_with_nb/<style> .post { max-width: min(800px, 100vw); overflow-x: hidden; } .post-content pre { max-width: 100%; overflow-x: auto; } .post-content table { display: block; max-width: 100%; overflow-x: auto; } .post-content p code, .post-content li code { white-space: break-spaces; overflow-wrap: anywhere; word-break: break-word; } .nb-math-scroll { max-width: 100%; overflow-x: auto; margin: 1rem 0; } .nb-math-inline { display: inline-flex; align-items: center; gap: 0.45rem; flex-wrap: nowrap; } .nb-math-stack { display: inline-flex; flex-direction: column; align-items: stretch; text-align: center; vertical-align: middle; } .nb-math-num, .nb-math-den { display: block; white-space: nowrap; } .nb-math-den { border-top: 1px solid currentColor; margin-top: 0.15rem; padding-top: 0.15rem; } </style> <p><a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier">Naive Bayes</a> is one of those wonderfully simple ideas in machine learning: look at the features of some input, estimate the probability of each possible class, and pick the most likely one. Its most famous use case is probably email spam detection.</p>