Welcome back for another edition of Under the Decision Tree. This week we had everything from machine learning being applied to cucumber farming, to major tech companies joining up to tackle the ethics of machine learning. Please send any suggestions to: Decision Tree We would love to hear from you.

Decision trees are used extensively in machine learning because they are easy to use, easy to interpret, and easy to operationalize. KD Nuggets, one of the most respected sites for data science and machine learning, recently published an article that identified decision trees as a "top 10" algorithm for machine learning. If you are new to machine learning, some of these concepts may be unfamiliar. The goal of this blog is to provide you with the basics of decision trees using Talend and Apache Spark. If you want to learn more about advanced analytics, please see the references section below.(2)

Last time we investigated the k-nearest-neighbors algorithm and the underlying idea that one can learn a classification rule by copying the known classification of nearby data points. This required that we view our data as sitting inside a metric space; that is, we imposed a kind of geometric structure on our data. One glaring problem is that there may be no reasonable way to do this. While we mentioned scaling issues and provided a number of possible metrics in our primer, a more common problem is that the data simply isn't numeric. For instance, a poll of US citizens might ask the respondent to select which of a number of issues he cares most about. There could be 50 choices, and there is no reasonable way to assign these numerical values so that all are equidistant in the resulting metric space. Another issue is that the quality of the data could be bad. For instance, there may be missing values for some attributes (e.g., a respondent may neglect to answer one or more questions).

Data Mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data. The tutorial starts off with a basic overview and the terminologies involved in data mining and then gradually moves on to cover topics such as knowledge discovery, query language, classification and prediction, decision tree induction, cluster analysis, and how to mine the Web. This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining. Before proceeding with this tutorial, you should have an understanding of the basic database concepts such as schema, ER model, Structured Query language and a basic knowledge of Data Warehousing concepts.

Decision Trees are a graphic and intuitive method of predicting the outcome of a given input. They attach a weightage to the input variables and help you clearly detect what really influences your outcome. Building a Decision Tree is a tedious procedure, as they have the tendency to overfit. That's where Random Forests come into the picture. Random Forests use an ensemble of Decision Trees, this reduces the complexities without compromising on the advantages.

Welcome back for another edition of Under the Decision Tree. As usual there were quite a number of interesting stories focused on machine learning and AI. One particularly interesting topic this week was Micorsoft and its efforts in cancer research. There are two conferences starting on Monday next week. Please send any suggestions to: Decision Tree We would love to hear from you.

Random forests or random decision forests[1][2] are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.[3]:587–588 The first algorithm for random decision forests was created by Tin Kam Ho [1] using the random subspace method,[2] which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.[4][5][6] An extension of the algorithm was developed by Leo Breiman[7] and Adele Cutler,[8] and "Random Forests" is their trademark.[9] The extension combines Breiman's "bagging" idea and random selection of features, introduced first by Ho[1] and later independently by Amit and Geman[10] in order to construct a collection of decision trees with controlled variance.

In supervised learning, the learning algorithm is provided outcome data in advance, in the form of a pre-labeled set of instances. It is from this set that the algorithm is expected to learn what to do when it encounters future, previously unseen instances. Classification is a form of supervised learning. As an example, take the biological taxonomic hierarchy. Organisms are grouped into successfully more specific ranks of domain, kingdom, phylum, etc.

Certain groups of people, such as women and children, might be entitled to receiving help first, granting them a higher chance of survival. Knowing whether you belong to one of these privileged groups would help predict whether you would make it out alive. To identify which groups have higher survival rates, we can use decision trees. While we forecast the rate of survival here, decision trees are used in a a wide range of applications. In the business setting, it can be used to define customer profiles or to predict who would resign.

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.