Decision Tree Learning


The Random Forest Algorithm – Towards Data Science

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Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it. Random Forest is a supervised learning algorithm. Like you can already see from it's name, it creates a forest and makes it somehow random.


Rule Induction Partitioning Estimator

arXiv.org Machine Learning

RIPE is a novel deterministic and easily understandable prediction algorithm developed for continuous and discrete ordered data. It infers a model, from a sample, to predict and to explain a real variable $Y$ given an input variable $X \in \mathcal X$ (features). The algorithm extracts a sparse set of hyperrectangles $\mathbf r \subset \mathcal X$, which can be thought of as rules of the form If-Then. This set is then turned into a partition of the features space $\mathcal X$ of which each cell is explained as a list of rules with satisfied their If conditions. The process of RIPE is illustrated on simulated datasets and its efficiency compared with that of other usual algorithms.


ANZ is using machine learning to improve the accuracy of data forecasting

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ANZ Bank has turned to machine learning to improve existing forecasting techniques. Economists Jack Chambers and David Plank applied the technique to monthly retail sales data, and compared it to the standard error found in consensus surveys compiled by Bloomberg. The machine learning process used by the pair was called "random forest". Think of a standard decision tree model, which maps out decisions or actions and their possible consequences. It follows that the "forest" is comprised of multiple decision trees, which are calculated and averaged to find correlations with retail sales.


Jointly learning relevant subgraph patterns and nonlinear models of their indicators

arXiv.org Machine Learning

Classification and regression in which the inputs are graphs of arbitrary size and shape have been paid attention in various fields such as computational chemistry and bioinformatics. Subgraph indicators are often used as the most fundamental features, but the number of possible subgraph patterns are intractably large due to the combinatorial explosion. We propose a novel efficient algorithm to jointly learn relevant subgraph patterns and nonlinear models of their indicators. Previous methods for such joint learning of subgraph features and models are based on search for single best subgraph features with specific pruning and boosting procedures of adding their indicators one by one, which result in linear models of subgraph indicators. In contrast, the proposed approach is based on directly learning regression trees for graph inputs using a newly derived bound of the total sum of squares for data partitions by a given subgraph feature, and thus can learn nonlinear models through standard gradient boosting. An illustrative example we call the Graph-XOR problem to consider nonlinearity, numerical experiments with real datasets, and scalability comparisons to naive approaches using explicit pattern enumeration are also presented.


4 Steps to Machine Learning with Pentaho

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At this stage, the practitioner might be satisfied with the analysis and be ready to build a final production-ready model. Clearly decision trees are performing best, but is there a (statistically) significant difference between the different implementations? Is it possible to improve performance further? There might be more than one dataset (from different stores/sites) that needs to be considered. In such situations, it is a good idea to perform a more principled experiment to answer these questions.


Machine learning predicts World Cup winner

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The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.


Machine learning predicts World Cup winner

#artificialintelligence

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.


Decision Trees in R

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Let's imagine you are playing a game of Twenty Questions. Your opponent has secretly chosen a subject, and you must figure out what he/she chose. At each turn, you may ask a yes-or-no question, and your opponent must answer truthfully. How do you find out the secret in the fewest number of questions? It should be obvious some questions are better than others. For example, asking "Can it fly?" as your first question is likely to be unfruitful, whereas asking "Is it alive?" is a bit more useful. Intuitively, you want each question to significantly narrow down the space of possibly secrets, eventually leading to your answer. That is the basic idea behind decision trees. At each point, you consider a set of questions that can partition your data set. You choose the question that provides the best split and again find the best questions for the partitions.


Machine learning predicts World Cup winner

#artificialintelligence

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.


2018 World Cup Predictions using decision trees

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In this study, we predict the outcome of the football matches in the FIFA World Cup 2018 to be held in Russia this summer. We do this using classification models over a dataset of historic football results that includes attributes from the playing teams by rating them in attack, midfield, defence, aggression, pressure, chance creation and building ability. This last training data was a result of merging international matches results with AE games ratings of the teams considering the timeline of the matches with their respective statistics. Final predictions show the four countries with the most chances of getting to the semifinals as France, Brazil, Spain and Germany while giving Spain as the winner. The objective of this study is to build a predictive model that will allow us to make good predictions for the coming World Cup 2018 so we looked for dataset with historic data for match results, for this purpose we chose a dataset from Kaggle with data of almost 40,000 international matches played between 1872 and 2018.