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Scikit-learn VS Draft

Compare Scikit-learn VS Draft and see what are their differences

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Draft logo Draft

A tool for developers to create cloud-native applications on Kubernetes
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Draft Landing page
    Landing page //
    2022-11-03

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Draft videos

2020 NHL Draft Recap/Review | Bob McKenzie & Craig Button

More videos:

  • Review - 2020 NFL Draft Grades
  • Review - NFL Players Read Their Negative Draft Reviews

Category Popularity

0-100% (relative to Scikit-learn and Draft)
Data Science And Machine Learning
Productivity
0 0%
100% 100
Data Science Tools
100 100%
0% 0
iPhone
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Draft

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

Draft Reviews

We have no reviews of Draft yet.
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Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Draft. While we know about 29 links to Scikit-learn, we've tracked only 2 mentions of Draft. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scikit-learn mentions (29)

  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 days ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 4 months ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago
  • WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
    The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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Draft mentions (2)

  • From Whispers to Wildfire: Celebrating a Decade of Kubernetes
    The fire continued to blaze onward. We created SIGs - Special Interest Groups - to gather people weekly or bi-weekly to discuss specific areas of interest. I co-created and co-led SIG-Apps. My interest was figuring out how to make it easy to build, install and manage applications in Kubernetes and the tools we needed on top of Kubernetes. I contributed to Helm and Draft in particular around this time as there was... - Source: dev.to / 19 days ago
  • Top 200 Kubernetes Tools for DevOps Engineer Like You
    Kubernetes on AWS (kube-aws) - A command-line tool to declaratively manage Kubernetes clusters on AWS Draft: Streamlined Kubernetes Development - A tool for developers to create cloud-native applications on Kubernetes Helm-ssm - A low dependency tool for retrieving and injecting secrets from AWS SSM into Helm Skupper - Multicloud communication for Kubernetes. - Source: dev.to / over 2 years ago

What are some alternatives?

When comparing Scikit-learn and Draft, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Boostnote - Boostnote is an open-source note-taking​ app.

OpenCV - OpenCV is the world's biggest computer vision library

Supernotes - The fastest way to take notes and collaborate with friends. Create notecards with Markdown, LaTeX, images, emojis and more. Get started for free!

NumPy - NumPy is the fundamental package for scientific computing with Python

Sleeperbot - Fantasy football leagues that don’t suck