Numerai – Gradient Boosting example.

In this post, I want to share, how simple it is to start competing in machine learning tournaments – Numerai. I will go step by step, line by line explaining what is doing what and why it is required.

Numerai is a global artificial intelligence competition to predict the behavior. Numerai is a little bit similar to Kaggle but with clean datasets, so we can pass over long data cleansing process.  You just download the data, build a model, and upload your predictions, that’s it. To extract most of the data you would initially do some feature engineering, but for simplicity of this intro, we will pass this bit over.  One more thing we will pass on is splitting out validation set, the main aim of this exercise is to fit ‘machine learning’ model to training dataset. Later using fitted model, generate a prediction.  All together it shouldn’t take more than 14 simple lines of python code, you can run them as one piece or run part by part in interactive mode.

Let’s go, let’s do some machine learning…

A first thing to do is to go to, click on ‘Download Training Data’  and download datasets, after unzipping the archive, you will have few files in there, we are interested mainly in three of them. It is worth noting what is a path to the folder as we will need it later.

I assume you have installed python and required libraries, if not there is plenty of online tutorials on how to do it, I recommend installing Anaconda distribution. It it time to open whatever IDE you use, and start coding, first few lines will be just importing what we will use later, that is Pandas and ScikitLearn.

Pandas is used to import data from csv files and do some basic data manipulations, GradientBoostingClassifier as part of ScikitLearn will be the model we will use to fit and do predict. As we have required libraries imported let’s use them… in next three lines, we will import data from csv to memory.  We will use ‘read_csv’  method from pandas, all you need to do is amend the full path to each file, wherever you have extracted

What above code does it creates three data frames and imports the csv files we have we have previously extracted from downloaded

‘train’ –  this dataset contains all required data to train our model, so it has both ‘features’ and ‘labels’, so you can say it has both questions and answers that our model will ‘learn’

‘test’ – this one contains features but does not contain ‘labels’, you can say it contains questions and our model will deliver answers.

‘sub’ – it is just template for uploading our prediction

Let’s move on,  in next line will copy all unique row id’s from ‘test’ to ‘sub’ to make sure each predicted value will be assigned to a right set of features, let’s say we put question number next to our answer so whoever checks the test would now.

As we have copied the ids to ‘sub’, we don’t need them anymore in ‘test’ (all rows will stay in same order), so we can get rid of them.

In next two lines, we will separate ‘labels’ or target values from train dataset.

As we have prepared ‘train’ dataset, we can get our model to learn from it. First, we select model we want to use, it will be Gradient BoostingClassifier from ScikitLearn – no specific reason for using this one, you can use whatever you like eg. random forest, linear regression…

As we have a model defined, let’s have it learn from ‘train’ data.

Ok, now our model is well trained and ready to make predictions, as the task is called ‘classification’ we will predict what is a probability of each set of features belongs to one of two classes ‘0’ or ‘1’.

We have a long list of predicted probabilities called ‘y_pred’, let’s attach it to ‘id’ we had separated previously.

And save it in csv format, to get uploaded.

The last thing to do is go back to website and click on ‘Upload Predictions’… Good luck.

This was very simplistic and introductory example to start playing with competitions and machine learning. I will try and come back with gradually more complicated versions, if you have any questions, suggestions or comments please go to ‘About’ section and contact me directly.

The full code below: