Blog

Published at: July 29, 2017, 2:07 p.m. | Author: Piotr Płoński

The churn prediction use case - done with MLJAR and its R-wrapper. The results are compared with h2o.ai. Article by Domink Krzemiński

Published at: May 19, 2017, 1:33 p.m. | Author: Piotr Płoński

The analytic methods can improve Human Resources (HR) management for companies with large number of employees. It is very easy to give example, how can companies benefit from machine learning methods applied to HR. Let’s assume that training of new employee costs 1000$ and if we can predict which employee is going to leave next month, and propose him/her a bonus program worth 500$ to keep him for next 6 months, we are 500$ on plus and keep experienced, well-trained employee under the hood, with higher morale.

Published at: Feb. 21, 2017, 8:34 p.m. | Author: Piotr Płoński

We are thrilled to announce our MLJAR python API. It makes building and tuning machine learning models super easy! You just write few lines of python code and all models are trained and tuned in the cloud on multiple machines and all results are available to check in your web browser! It is very powerful! :) You can check it on mljar github: https://github.com/mljar

Published at: Dec. 12, 2016, 1:33 p.m. | Author: Piotr Płoński

Herein the performance of MLJAR on Kaggle dataset from “Give me some credit” challenge is reported. The obtained results are compared with other predictive APIs from Amazon, Google, PredicSis and BigML. This post was inspired with Louis Dorard's [article][1]. [1]: http://www.kdnuggets.com/2015/05/machine-learning-wars-amazon-google-bigml-predicsis.html

Published at: Nov. 9, 2016, 4:40 p.m. | Author: Piotr Płoński

Dyslexia is reading disorder - characterized by trouble with reading despite normal intelligence. We used ML methods to find brain regions responsible for this! Check out the paper: "Multi-Parameter Machine Learning Approach to the Neuroanatomical Basis of Developmental Dyslexia" in Human Brain Mapping journal.

Published at: Nov. 9, 2016, 4:20 p.m. | Author: Piotr Płoński

We made a youtube video with instruction how to build a binary classifier on Numer.ai dataset. We use raw data - but you should make some magic, create new features and use MLJAR to find the best models!

Published at: Oct. 28, 2016, 10:56 a.m. | Author: Piotr Płoński

MLJAR is a platform for rapid prototyping, developing and deploying machine learning models. Yeah! Here we list MLJAR rationale.

Published at: Sept. 28, 2016, 1:30 p.m. | Author: Piotr Płoński

Machine learning models used by hedge funds for predicting stock market are of course super top secret as well as data used for their creation. However, there is one which makes its data public - Numer.ai. Dataset is encrypted and prediction of stock market is transformed into binary classification problem. Every 7 days (one round time) a new dataset is released and anyone can download it, train model and upload predictions. At the end of round, the best predictions are rewarded - there is no need to model upload. As you can see from leaderboard mljar.com is going really well in this competition.

Published at: Sept. 27, 2016, 7:11 a.m. | Author: Piotr Płoński

MLJAR is a framework for building machine learning models - it is done fast, accurate, easy and (almost) automatic. We hope it can help many data hackers. In this blog we are going to describe interesting (in our opinion) use cases - so be in touch with us!