BBerkan Sesen·Jul 5Handling Class Imbalance: Downsampling, SMOTE, and BeyondCompare five strategies for imbalanced classification: random downsampling, upsampling, SMOTE, and class weights. Evaluate with precision-recall curves and F1, not just accuracy.13 min read·sesenai.hashnode.dev
BBerkan Sesen·Jun 29AIC and BIC: Choosing the Right Model Without OverfittingDerive the log-likelihood from first principles, then build AIC and BIC as penalised model selection criteria. Compare polynomial fits, visualise the bias-variance tradeoff, and understand when to use each criterion.13 min read·sesenai.hashnode.dev
BBerkan Sesen·Apr 17Text Classification from Scratch: TF-IDF and Naive BayesBuild a text classifier that sorts raw documents into 20 categories. Learn how TF-IDF turns text into numbers and why Naive Bayes works surprisingly well despite its 'naive' assumption.16 min read·sesenai.hashnode.dev1
