English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Most automation in machine learning focuses on model selection and hyperparameter tuning, and many overlook the challenge of automatically definingpredictive tasks. We still heavily rely on human experts to define predictiontasks, and generate labels by aggregating raw data. In this paper, we tacklethe challenge of defining useful prediction problems on event-driventime-series data. We introduce MLFriend to address this challenge. MLFriendfirst generates all possible prediction tasks under a predefined space, theninteracts with a data scientist to learn the context of the data and recommendgood prediction tasks from all the tasks in the space. We evaluate our systemon three different datasets and generate a total of 2885 prediction tasks andsolve them. Out of these 722 were deemed useful by expert data scientists. Wealso show that an automatic prediction task discovery system is able toidentify top 10 tasks that a user may like within a batch of 100 tasks.

MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data