---
title: "#discriminative articles | Hashnode"
description: "Explore 3 articles tagged #discriminative on Hashnode. Tutorials, guides, and how-tos from the community."
url: "https://hashnode.com/tag/discriminative"
---

Tag

# #discriminative

3 posts · 0 followers

[Berkan Sesen](https://hashnode.com/@berkansesen) · [Jul 5](https://hashnode.com/posts/handling-class-imbalance-downsampling-smote-and-beyond/6a4a4b6560ef028b52111076)

### [Handling Class Imbalance: Downsampling, SMOTE, and Beyond](https://sesenai.hashnode.dev/handling-class-imbalance-downsampling-smote-and-beyond?utm_source=hashnode&utm_medium=feed)

Compare 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](https://sesenai.hashnode.dev/handling-class-imbalance-downsampling-smote-and-beyond?utm_source=hashnode&utm_medium=feed)

[Berkan Sesen](https://hashnode.com/@berkansesen) · [Jun 29](https://hashnode.com/posts/aic-and-bic-choosing-the-right-model-without-overfitting/6a422fe2328f4f8cb9f3575d)

### [AIC and BIC: Choosing the Right Model Without Overfitting](https://sesenai.hashnode.dev/aic-and-bic-choosing-the-right-model-without-overfitting?utm_source=hashnode&utm_medium=feed)

Derive 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](https://sesenai.hashnode.dev/aic-and-bic-choosing-the-right-model-without-overfitting?utm_source=hashnode&utm_medium=feed)

[Berkan Sesen](https://hashnode.com/@berkansesen) · [Apr 17](https://hashnode.com/posts/text-classification-from-scratch-tf-idf-and-naive-bayes/69e22b9b66d26d064ad66d69)

### [Text Classification from Scratch: TF-IDF and Naive Bayes](https://sesenai.hashnode.dev/text-classification-from-scratch-tf-idf-and-naive-bayes?utm_source=hashnode&utm_medium=feed)

Build 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.dev](https://sesenai.hashnode.dev/text-classification-from-scratch-tf-idf-and-naive-bayes?utm_source=hashnode&utm_medium=feed)