Tag feed

#scikit-learn

129 posts51 followers

Explore Hashnode

Alternatives

Trending tags this week

AKAkash Kumarakash-1.hashnode.dev6d ago · 7 min read

Confusion Matrix Explained with Examples | Machine Learning Evaluation Guide

Confusion Matrix Explained with Examples When building a machine learning classification model, simply looking at accuracy isn't enough. Imagine a model that predicts whether a patient has cancer. If

0

SSimonmy-ai-learning-plan.hashnode.devJul 4 · 7 min read

Building Decision Trees with Pandas and Scikit-learn

The Theory So what's a Decision Tree Regressor and how does it work?It's a regression model so it returns continuous numerical values. It gets a prediction by asking a series of yes/no questions about

0

SSSilent Scribesilentscribejournal.hashnode.devJul 4 · 4 min read

From Study Hours to Scores: How Linear Regression Predicts the Future

Imagine you're a parent trying to guess how many marks your child might score based on how many hours they study. You can't know the exact number, but you can make a smart, data-driven estimate — and

0

WTwajahat tajai-ml-datascience-from-scratch.hashnode.devJun 16 · 6 min read

Building a Municipal AI Complaint System FastAPI, ML, PostgreSQL, and Docker

I have been working on a project that I think is actually useful in a real-world context, a municipal complaint management system that uses machine learning to automatically categorize, score, and pri

0

NNNaguru Navya Nikhithaneuralnotes-ai.hashnode.devJun 1 · 4 min read

10 Scikit-learn Functions Every ML Beginner Should Know

You've cleaned your data. You've visualized it. Now it's time to build models. Scikit-learn is the most used machine learning library in Python. Here are the 10 functions you'll use in every single ML

0

MMMungara Muddu Krishna Yadavmuddukrishna.hashnode.devMay 19 · 13 min read

Building Bulletproof ML Pipelines: Feature Engineering, Scaling & Leakage-Free Preprocessing

Part 2 of the ML Engineering series — where clean data meets production-ready code. Why pipelines matter more than your algorithm choice There's a humbling pattern every ML engineer eventually faces:

0

RARoberto Andrade Fonsecarandradedev.hashnode.devApr 29 · 5 min read

Heil Mary. Repensando la enseñanza de la física desde la ciencia ficción

He pasado años enseñando ciencia y tecnología. Además soy un lector frecuente de la ciencia ficción, a veces con la misma intensidad con la que preparo una clase o aprendo sobre TIC o AI . En algún pu

0

HHHenry Hadatabeginnersfriendlytutorials.hashnode.devApr 15 · 26 min read

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

When building a machine learning model, it's tempting to train it on your data, check the score, and call it done. But that score is misleading — the model has already "seen" the data it's being evalu

0

AAAbstract Algorithmsabstractalgorithms.hashnode.devMar 29 · 19 min read

Feature Engineering: Transforming Raw Data into ML-Ready Features

TLDR: 🛠️ Feature engineering transforms messy real-world data into ML-compatible input. Bad features break even the best models — good features make simple algorithms shine. This guide covers scaling

0

DSDumpali Sharath chandra Reddyhar-sharathcd.hashnode.devMar 16 · 6 min read

How I Benchmarked 15 Machine Learning Algorithms for Human Activity Recognition — and Hit 0.9682 Precision

By Sharath Chandra Reddy Dumpali · M.S. Computer Science, University of New Haven The Problem Your smartphone knows when you're walking, running, or sitting — but how does it actually figure that out

0

#scikit-learn

Search Hashnode

#scikit-learn

Explore Hashnode

Trending tags this week

Confusion Matrix Explained with Examples | Machine Learning Evaluation Guide

Building Decision Trees with Pandas and Scikit-learn

From Study Hours to Scores: How Linear Regression Predicts the Future

Building a Municipal AI Complaint System FastAPI, ML, PostgreSQL, and Docker

10 Scikit-learn Functions Every ML Beginner Should Know

Building Bulletproof ML Pipelines: Feature Engineering, Scaling & Leakage-Free Preprocessing

Heil Mary. Repensando la enseñanza de la física desde la ciencia ficción

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

Feature Engineering: Transforming Raw Data into ML-Ready Features

How I Benchmarked 15 Machine Learning Algorithms for Human Activity Recognition — and Hit 0.9682 Precision