The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. 3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week.

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models.

This is a guest post by Erik Meijer (@headinthebox). He is an accomplished programming-language designer who runs the Cloud Programmability Team at Microsoft and a professor of Cloud Programming at TUDelft. There is a lot of hype and mystique around Machine Learning these days. The combination of the words "machine" and "learning" induces hallucinations of intelligent machines that magically learn by soaking up Big Data and then both solving world hunger and making us rich while we lay on the beach sipping a cold one. However, just as normal programmers can write code without needing to understand Universal Turing Machines, power domains, or predicate transformers, we believe that normal programmers can use Machine Learning without needing to understand vectors, features, probability density, Jacobians, etc.

This is a tough task, I was in this precarious situation trying to explain to my younger son. I had a curated list of top 10 frequently used Machine Learning algorithms, but the key was to do a backward mapping of these Machine Learning techniques to solve problems which are of interest and relevance to my son. As a starting point to the conversation I asked him, list down your decision making points, meaning there may be many situations when you had to make decisions but you may not have all the information. I took those and matched it to the machine learning algorithms while explaining the core concept behind the problem solving. A classifier is a machine learning technique that takes a bunch of data and attempts to predict which class the new data belongs to. Sure, remember sometime back you were asking me who you want to invite to your birthday party and whether they will accept your invitation or not! Now, assume that you have got a data set about all the other 29 kids in your class. The information contains their hobby, kind of books they read, do they share their tiffin or not, are they friendly, in your last birthday did they come and did they bring nice gifts, etc. Now, given these information, you want to predict whether your classmates will accept your invitation or not.

Gradient boosting (GB) is a machine learning algorithm developed in the late '90s that is still very popular. It produces state-of-the-art results for many commercial (and academic) applications. This page explains how the gradient boosting algorithm works using several interactive visualizations. We take a 2-dimensional regression problem and investigate how a tree is able to reconstruct the function \( y f(\vx) f(x_1, x_2) \). Play with the tree depth, then look at the tree-building process from above!

Numerai is synthesizing machine intelligence to command the capital of an American hedge fund. In the late 90s and early 2000s, new algorithms such as AdaBoost and Random Forests made breakthroughs in machine learning. The principle behind each of these algorithms was very simple: build many decision tree models that each learn something different, and then average them all together to create an ensemble model. These algorithms worked incredibly well. They were easy to understand, and computationally efficient.

Gradient boosting involves the creation and addition of decision trees sequentially, each attempting to correct the mistakes of the learners that came before it. This raises the question as to how many trees (weak learners or estimators) to configure in your gradient boosting model and how big each tree should be. In this post you will discover how to design a systematic experiment to select the number and size of decision trees to use on your problem. How to Tune the Number and Size of Decision Trees with XGBoost in Python Photo by USFWSmidwest, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

In this lesson we are going to walk through the exciting topic of big data analysis. At a high level, big data analysis is the process that helps to build algorithms that can analyze vast amounts of data and be able to generate behavior-based decisions from that data. For big data analysis in this section, we'll be using the Decision Tree gem. It uses the ID3 algorithm and is very efficient in taking data and making decisions based on it. I'd like to show you a practical big data project I built for a client about a year ago.

This column is by Matt Fogel, Co-Founder, Fuzzy.io Data science and machine learning have long been interests of mine, but now that I'm working on Fuzzy.ai I need to keep on top of all the news in both fields. My preferred way to do this is through listening to podcasts. I've listened to a bunch of machine learning and data science podcasts in the last few months, so I thought I'd share my favorites: Every other week, they release a 10–15 minute episode where hosts, Kyle and Linda Polich give a short primer on topics like k-means clustering, natural language processing and decision tree learning, often using analogies related to their pet parrot, Yoshi.

"Machine Learning" (ML) methods have been around for ages but Big Data revolution and plummeting cost of computing power are now making them truly excellent and practical analytical tools in banking, across a variety of use cases, including credit risk. ML algorithms may sound complex and futuristic but the way they work is quite simple. Essentially they combine a massive set of decision trees (i.e., a decision-making model that breaks out individual decisions and possible consequences, as known as "learners") to create an accurate model. By churning through these learners at high speeds, ML models are able to find "hidden" patterns, particularly in unstructured data that common statistical tools miss. Overfitting (the analytical description of random errors instead of underlying relationships) of the model is a typical concern that comes up in regards to ML. Overfitting of ML models can be avoided by carefully choosing input variables and the specific algorithm used.