Blog


Churn Prediction with Automatic ML


Published at: Sept. 27, 2017, 8:01 a.m. | Author: Dominik Krzemiński


Sometimes we don’t even realize how common machine learning (ML) is in our daily lives. Various “intelligent” algorithms help us for instance with finding the most important facts (Google), they suggest what movie to watch (Netflix), or influence our shopping decisions (Amazon). The biggest international companies quickly recognized the potential of machine learning and transferred it to business solutions. Nowadays not only big companies are able to use ML. Imagine — not so abstract — situation when a company tries to predict customer behavior based on some personal data. Just a few years ago, the best strategy to solve this problem would be to hire a good data science team. Nowadays, thanks to growing ML popularity, it is available even for small start-ups. Today, I would like to present you a demo of how to solve difficult business problems with ML. We will take advantage of mljar.com service and its R API. With just a few lines of code we will be able to achieve very good results.




MLJAR R API


Published at: Sept. 20, 2017, 10:09 a.m. | Author: Piotr Płoński


Hi! We have added R API for mljar - so you can run sklearn, xgboost, lightGBM, Keras, RGF from one R line :) Please check it on https://github.com/mljar/mljar-api-R




Are hyper-parameters really important in Machine Learning?


Published at: Aug. 22, 2017, 10:20 a.m. | Author: Dominik Krzemiński


It seems that one of the most problematic topics for machine-learning self-learners is to understand the difference between parameters and hyper-parameters. The concept of hyper-parameters is very important, because these values directly influence overall performance of ML algorithms.The simplest definition of hyper-parameters is that they are a special type of parameters that cannot be inferred from the data. Imagine, for instance, a neural network. As you probably know, artificial neurons learning is achieved by tuning their weights in a way that the network gives the best output label in regard to the input data. However, architectures of neural networks vary depending on the task. There are many things to be considered: number of layers, size of each layer, number of connections, etc. They need to be considered before network tuning, so in this case they are called hyper-parameters.




Employee Analytics


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.




MLJAR python API


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




Machine Learning Wars


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




Searching for brain regions responsible for kids dyslexia


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.




Building Binary Classifier on Numer.ai data


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!




MLJAR Rationale


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.




Predict stock market on AI tournament


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.




3, 2, 1 - Start! MLJAR Blog


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!