yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.

  •        19

Visual analysis and diagnostic tools to facilitate machine learning model selection. Image by Quatro Cinco, used with permission, Flickr Creative Commons.

http://www.scikit-yb.org/
https://github.com/DistrictDataLabs/yellowbrick

Tags
Implementation
License
Platform

   




Related Projects

Scikit Learn - Machine Learning in Python

  •    Python

scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.

tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

  •    Python

Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.


linear_regression_demo - This is the code for "How to Make a Prediction - Intro to Deep Learning #1' by Siraj Raval on YouTube

  •    Python

##Overview This is the code for this video by Siraj Raval on Youtube. This is the 1st episode in my 'Intro to Deep Learning' series. The goal is to predict an animal's body weight given it's brain weight. The model we'll be using is called Linear Regression. The dataset we're using to train our model is a list of brain weight and body weight measurements from a bunch of animals. We'll fit our line to the data using the scikit learn machine learning library, then plot our graph using matplotlib. You can just run pip install -r requirements.txt in terminal to install the necessary dependencies. Here is a link to pip if you don't already have it.

mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

  •    Python

This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

  •    Jupyter

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

sklearn2pmml - Python library for converting Scikit-Learn pipelines to PMML

  •    Python

Python library for converting Scikit-Learn pipelines to PMML. This library is a thin wrapper around the JPMML-SkLearn command-line application. For a list of supported Scikit-Learn Estimator and Transformer types, please refer to the documentation of the JPMML-SkLearn project.

scikit-plot - An intuitive library to add plotting functionality to scikit-learn objects.

  •    Python

Scikit-plot is the result of an unartistic data scientist's dreadful realization that visualization is one of the most crucial components in the data science process, not just a mere afterthought. Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a single-line dump of numbers enclosed in brackets. Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel.

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

  •    Jupyter

This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

astroML - Machine learning, statistics, and data mining for astronomy and astrophysics

  •    Python

AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets. This project was started in 2012 by Jake VanderPlas to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.

skll - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

  •    Python

This Python package provides command-line utilities to make it easier to run machine learning experiments with scikit-learn. One of the primary goals of our project is to make it so that you can run scikit-learn experiments without actually needing to write any code other than what you used to generate/extract the features. For more information about getting started with run_experiment, please check out our tutorial, or our config file specs.

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

Python-Machine-Learning-Cookbook - Code files for Python-Machine-Learning-Cookbook

  •    Python

##Instructions and Navigation This is the code repository for Python Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. The code files are organized according to the chapters in the book. These code samples will work on any machine running Linux, Mac OS X, or Windows. Even though they are written and tested on Python 2.7, you can easily run them on Python 3.x with minimal changes. To run the code samples, you need to install scikit-learn, NumPy, SciPy, and matplotlib. For Chapter 6, you will need to install NLTK and gensim. To run the code in chapter 7, you need to install hmmlearn and python_speech_features. For chapter 8, you need to install Pandas and PyStruct. Chapter 8 also makes use of hmmlearn. For chapters 9 and 10, you need to install OpenCV. For chapter 11, you need to install NeuroLab.

auto_ml - Automated machine learning for analytics & production

  •    Python

auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

palladium - Framework for setting up predictive analytics services

  •    Python

Palladium provides means to easily set up predictive analytics services as web services. It is a pluggable framework for developing real-world machine learning solutions. It provides generic implementations for things commonly needed in machine learning, such as dataset loading, model training with parameter search, a web service, and persistence capabilities, allowing you to concentrate on the core task of developing an accurate machine learning model. Having a well-tested core framework that is used for a number of different services can lead to a reduction of costs during development and maintenance due to harmonization of different services being based on the same code base and identical processes. Palladium has a web service overhead of a few milliseconds only, making it possible to set up services with low response times. A configuration file lets you conveniently tie together existing components with components that you developed. As an example, if what you want to do is to develop a model where you load a dataset from a CSV file or an SQL database, and train an SVM classifier to predict one of the rows in the data given the others, and then find out about your model's accuracy, then that's what Palladium allows you to do without writing a single line of code. However, it is also possible to independently integrate own solutions.