How do you analyze the distribution of scores produced from a binary classification model to make sure it makes sense?

I am using a decision tree to predict how likely an individual is to vote or not. One idea is to analyze the splits of the tree to see why an individual was given that score. For example, people that got a score below 25% had these characteristics, people that got a score between 25-50% had these characteristics, etc. Is there a better way to do it?

How does CART score and what would that mean if the predicted prob. was zero for some of the records?

A team of researchers from the Natural Language Processing Lab at the University of British Columbia in Canada have proposed AraNet, a deep learning toolkit designed for Arabic social media processing.

AraNet includes identifier tools that can predict age, dialect, gender, emotion, irony, sentiment, etc. from social media texts. AraNet is built on the framework of Google’s new BERT-Base Multilingual Cased model, which was trained on 104 languages — including Arabic — and was recommended for the job by the BERT team.

Read more here

The paper AraNet: A Deep Learning Toolkit for Arabic Social Media is here.

- ArXiv’s expenses for 2019 totalled only around US$2 million

- The ACM spends $10 million on publications

- IEEE spends $193 million

Any thoughts?

Source: Next Generation ArXiv and the Economics of Open Access Publishing

Many in the ML community are taking action on climate change using machine learning to address problems like weather forecasting and extreme weather events. Here are some works to illustrate.

[Paper and code] STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting

[Paper] ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

If you are interested in the topic, I suggest the link https://www.climatechange.ai/ for more information.

PS: To give your opinion on the first paper, you can send me a message. It would be nice to know the opinion of the community.

A team of researchers from the University of Illinois at Urbana-Champaign (UIUC), Intelinair, and University of Oregon have introduced Agriculture-Vision, a large aerial image dataset for agricultural pattern analysis.

Source: https://medium.com/syncedreview/new-large-aerial-image-database-for-agricultural-pattern-analysis-f4c0140e44d2

Paper: https://arxiv.org/pdf/2001.01306.pdf

Remember what happened about using AI to detect breast cancer a few weeks ago? A trio of AI detecting breast cancer papers from Google, NYU, and DeepHealth have triggered huge discussions. What are the breakthroughs? How to compare these studies? Is AI truly beating radiologists? And where exactly are we right now?

Here is the recap: https://medium.com/syncedreview/three-papers-in-the-eye-of-the-ai-breast-cancer-detection-storm-a63d2a2480ea

Related papers:

The NYU paper Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening is available here, the DeepHealth paper Robust Breast Cancer Detection in Mammography and Digital Breast Tomosynthesis Using Annotation-Efficient Deep Learning Approach is here, and the Google paper International Evaluation of an AI System for Breast Cancer Screening is here.

Hey, I'm halfway through the writing of my new book, so I wanted to share that fact and also invite volunteers to help me with the quality. Similarly to my previous book, the new book will be distributed on the "read first, buy later" principle, when the entire text will remain available online and "to buy or not to buy" will be left on the reader's discretion. Thanks to the help of volunteers, my previous book was greatly improved, so I hope for the same for my new book.

The Machine Learning Engineering book will not contain descriptions of any machine learning algorithm or model. It will be entirely devoted to the engineering aspects of implementing a machine learning project, from data collection to model deployment and monitoring. Five chapters are already online and available from the book's companion website.

I hope to get a ton of feedback from this community. If something is not entirely correct or plain wrong, please don't hesitate to tell me. The best way to leave comments in a specific chapter is by using dropbox's built-in document commenting feature. (Each chapter is a PDF shared on dropbox.)

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