--- title: "#scikit-learn articles | Hashnode" description: "Explore 145 articles tagged #scikit-learn on Hashnode. Tutorials, guides, and how-tos from the community." url: "https://hashnode.com/tag/scikit-learn" --- Tag # #scikit-learn 145 posts · 51 followers [Omar Essam El Sherbiny](https://hashnode.com/@omaressam) · [Oct 30](https://hashnode.com/posts/churn-prediction-where-the-simple-model-wins-logistic-regression-against-five-rivals/6ae430f75545bb9a9b844cb4) ### [Churn prediction where the simple model wins: logistic regression against five rivals](https://omaressam.hashnode.dev/churn-prediction-where-the-simple-model-wins-logistic-regression-against-five-rivals?utm_source=hashnode&utm_medium=feed) Six model families tuned on the same folds; the top three are statistically tied, so the readable one gets the job. 4 min read · [omaressam.hashnode.dev](https://omaressam.hashnode.dev/churn-prediction-where-the-simple-model-wins-logistic-regression-against-five-rivals?utm_source=hashnode&utm_medium=feed) [Kelvin R. Tobias](https://hashnode.com/@kelvinintech) · [Oct 19](https://hashnode.com/posts/tough-vikings-take-delayed-icebaths/6ad673598e97208120682bb0) ### [Tough Vikings Take Delayed Icebaths 🧊](https://kelvinintech.hashnode.dev/tough-vikings-take-delayed-icebaths?utm_source=hashnode&utm_medium=feed) Learn the machine learning lifecycle with a Viking analogy. 17 min read · [kelvinintech.hashnode.dev](https://kelvinintech.hashnode.dev/tough-vikings-take-delayed-icebaths?utm_source=hashnode&utm_medium=feed) [Bahati Brenda Kizito](https://hashnode.com/@Lioness256) · [Oct 12](https://hashnode.com/posts/ml-pipeline-explained/6ace1105f9099f3521dd082a) ### [I Finally Understand What an ML Pipeline Actually Does](https://godfident-data.hashnode.dev/ml-pipeline-explained?utm_source=hashnode&utm_medium=feed) Learn what a machine learning pipeline does, from data validation, preprocessing, training, evaluation, deployment, monitoring. 19 min read · [godfident-data.hashnode.dev](https://godfident-data.hashnode.dev/ml-pipeline-explained?utm_source=hashnode&utm_medium=feed) [Libraryminds](https://hashnode.com/@libraryminds) · [Oct 5](https://hashnode.com/posts/semantic-search-over-transcripts-in-python/6ac3469f8ffc43b6b8fbc33a) ### [Semantic Search Over Transcripts in Python: What Embeddings Fix and What They Don't](https://libraryminds.hashnode.dev/semantic-search-over-transcripts-in-python?utm_source=hashnode&utm_medium=feed) Keyword, vector and hybrid search on a 28-paragraph transcript in Python, with real output, honest misses, and code you can run. 24 min read · [libraryminds.hashnode.dev](https://libraryminds.hashnode.dev/semantic-search-over-transcripts-in-python?utm_source=hashnode&utm_medium=feed) [5](https://hashnode.com/posts/semantic-search-over-transcripts-in-python/6ac3469f8ffc43b6b8fbc33a) [Faith Mwende](https://hashnode.com/@mwendekiindu) · [Sep 29](https://hashnode.com/posts/i-finally-understand-auc-confusion-matrix-and-classification-report-day-4/6abbc36881e777c6e43370c8) ### [I Finally Understand AUC, Confusion Matrix, and Classification Report (Day 4)](https://beginnerslog.hashnode.dev/i-finally-understand-auc-confusion-matrix-and-classification-report-day-4?utm_source=hashnode&utm_medium=feed) My first fraud detection model scored 0.9571 AUC and 99.9% accuracy but missed 40% of frauds. 4 min read · [beginnerslog.hashnode.dev](https://beginnerslog.hashnode.dev/i-finally-understand-auc-confusion-matrix-and-classification-report-day-4?utm_source=hashnode&utm_medium=feed) [Faith Mwende](https://hashnode.com/@mwendekiindu) · [Sep 29](https://hashnode.com/posts/splitting-fraud-data-without-breaking-the-ratio-day-3/6abbac6a3973b25f7d9644c3) ### [Splitting Fraud Data Without Breaking the Ratio (Day 3)](https://beginnerslog.hashnode.dev/splitting-fraud-data-without-breaking-the-ratio-day-3?utm_source=hashnode&utm_medium=feed) Why stratify=y is the most important argument in my train/test split. 3 min read · [beginnerslog.hashnode.dev](https://beginnerslog.hashnode.dev/splitting-fraud-data-without-breaking-the-ratio-day-3?utm_source=hashnode&utm_medium=feed) [Muhammad Ariel Shakaramiro](https://hashnode.com/@arielshakaramiro) · [Sep 6](https://hashnode.com/posts/text-vectorizer-bow-tfidf-word-embedding-explained/6a9ce37dcbf4b05f822ea2d1) ### [Text Vectorizer Explained: BoW, N-gram, TF-IDF, and Word Embedding](https://shaka-ai.hashnode.dev/text-vectorizer-bow-tfidf-word-embedding-explained?utm_source=hashnode&utm_medium=feed) What's the difference between Bag of Words, TF-IDF, and Word Embedding? NLP study notes with verified code and real execution charts. 6 min read · [shaka-ai.hashnode.dev](https://shaka-ai.hashnode.dev/text-vectorizer-bow-tfidf-word-embedding-explained?utm_source=hashnode&utm_medium=feed) [Muhammad Ariel Shakaramiro](https://hashnode.com/@arielshakaramiro) · [Sep 6](https://hashnode.com/posts/intent-classification-tfidf-logistic-regression/6a9d27d4bf1bdadb44d89ea5) ### [Intent Classification with TF-IDF + Logistic Regression](https://shaka-ai.hashnode.dev/intent-classification-tfidf-logistic-regression?utm_source=hashnode&utm_medium=feed) 100% accuracy looks perfect, but what does it actually mean? Cross-validation audit and edge-case testing on Indonesian intent classification. 4 min read · [shaka-ai.hashnode.dev](https://shaka-ai.hashnode.dev/intent-classification-tfidf-logistic-regression?utm_source=hashnode&utm_medium=feed) [Muhammad Ariel Shakaramiro](https://hashnode.com/@arielshakaramiro) · [Sep 4](https://hashnode.com/posts/hands-on-iris-classification-logistic-regression-vs-decision-tree/6a9a365c76ba15391eade002) ### [Hands-On: Classifying Iris Flowers — Logistic Regression vs Decision Tree](https://shaka-ai.hashnode.dev/hands-on-iris-classification-logistic-regression-vs-decision-tree?utm_source=hashnode&utm_medium=feed) A hands-on ML tutorial: classifying Iris flowers with Logistic Regression and Decision Tree, complete with Python code and verified accuracy. 10 min read · [shaka-ai.hashnode.dev](https://shaka-ai.hashnode.dev/hands-on-iris-classification-logistic-regression-vs-decision-tree?utm_source=hashnode&utm_medium=feed) [3](https://hashnode.com/posts/hands-on-iris-classification-logistic-regression-vs-decision-tree/6a9a365c76ba15391eade002) [Muhammad Ariel Shakaramiro](https://hashnode.com/@arielshakaramiro) · [Sep 4](https://hashnode.com/posts/hands-on-unsupervised-learning-clustering-iris-kmeans/6a9a381330ff73fa6dc5d6be) ### [Hands-On: Unsupervised Learning — Clustering Iris Flowers with KMeans (Plus Follow-Up Experiments)](https://shaka-ai.hashnode.dev/hands-on-unsupervised-learning-clustering-iris-kmeans?utm_source=hashnode&utm_medium=feed) Hands-on clustering with no labels using KMeans on the Iris dataset, evaluated with Adjusted Rand Index, plus Random Forest and scaling experiments. 11 min read · [shaka-ai.hashnode.dev](https://shaka-ai.hashnode.dev/hands-on-unsupervised-learning-clustering-iris-kmeans?utm_source=hashnode&utm_medium=feed)