Published at: Dec. 7, 2017, 8:44 p.m. | Author: Piotr Płoński
Automatic Machine Learning(autoML) is a process of building Machine Learning models by algorithm with no human supervision. We compare three autoML packages (auto-sklearn, h2o and mljar). The comparison was performed on binary classification task on 28 datasets from openml.
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: 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]. : 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: 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.