Random Forest Classifier for Sports Betting

Random Forest

1. What is Random forests

Random forest is a supervised learning algorithm. As the name implies, Random Forest uses "Tree" as its foundation.

Random forests are a set of Decision Trees, each of which is selected by a random algorithm.

2. What is Decision Tree?

Decision Tree is the name that represents a group of algorithms that evolve based on Decision Trees. There, each Node of the tree will be properties, and branches are the selected value of that property. By following the property values ​​on the tree,

The decision tree will tell us the value of the prediction.

Decision tree algorithm group has a strong point that can be used for both Classification problem (Classification) and Regression (Regression).

3. What are the strengths of Random Forest?

• The Random Forest algorithm can be used for both Classification and Regression problems
• Random Forest works with data that lacks value
• When the Forest has more trees, we can avoid overfitting with the dataset
• Can create models for categorical values

4. How does Random Forest work?

We can think of a simple example in life, if I want to learn a place for my next travel, I will go ask a friend to consult.

But, this friend's opinion may not be very objective. I immediately went to ask a few more people, and combined them to make a decision or not

If we consider each of the commenters' opinions a decision tree, then we have a vague picture of Random Forest.

Random Forest works by evaluating multiple random Decision Trees, and extracting the best rated result out of the results.

5. BS information about Random Forest from Wikipedia for your reference

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees. However, data characteristics can affect their performance. 

The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.

An extension of the algorithm was developed by Leo Breiman and Adele Cutler, who registered "Random Forests" as a trademark in 2006 (as of 2019, owned by Minitab, Inc.). The extension combines Breiman's "bagging" idea and random selection of features, introduced first by Ho and later independently by Amit and Geman in order to construct a collection of decision trees with controlled variance.

Random forests are frequently used as "blackbox" models in businesses, as they generate reasonable predictions across a wide range of data while requiring little configuration.

The general method of random decision forests was first proposed by Ho in 1995. Ho established that forests of trees splitting with oblique hyperplanes can gain accuracy as they grow without suffering from overtraining, as long as the forests are randomly restricted to be sensitive to only selected feature dimensions. A subsequent work along the same lines concluded that other splitting methods behave similarly, as long as they are randomly forced to be insensitive to some feature dimensions. Note that this observation of a more complex classifier (a larger forest) getting more accurate nearly monotonically is in sharp contrast to the common belief that the complexity of a classifier can only grow to a certain level of accuracy before being hurt by overfitting. The explanation of the forest method's resistance to overtraining can be found in Kleinberg's theory of stochastic discrimination.  

The early development of Breiman's notion of random forests was influenced by the work of Amit and Geman who introduced the idea of searching over a random subset of the available decisions when splitting a node, in the context of growing a single tree. The idea of random subspace selection from Ho was also influential in the design of random forests. In this method a forest of trees is grown, and variation among the trees is introduced by projecting the training data into a randomly chosen subspace before fitting each tree or each node. Finally, the idea of randomized node optimization, where the decision at each node is selected by a randomized procedure, rather than a deterministic optimization was first introduced by Dietterich. 

The introduction of random forests proper was first made in a paper by Leo Breiman. This paper describes a method of building a forest of uncorrelated trees using a CART like procedure, combined with randomized node optimization and bagging. In addition, this paper combines several ingredients, some previously known and some novel, which form the basis of the modern practice of random forests, in particular:

• Using out-of-bag error as an estimate of the generalization error.
• Measuring variable importance through permutation.

The report also offers the first theoretical result for random forests in the form of a bound on the generalization error which depends on the strength of the trees in the forest and their correlation.

The Zcode Program also uses math "Random Forest Classifier" for sports betting

Random Forest Classifier for Sports Betting - zCode System™: https://zcodesystem.com/

Sports Match Result Prediction Using the Random Forest Classifier

Nowadays, people are turning their attention to football as the business investment more and more, especially invest in joining football club. In addition, If the results of some matches do not meet the goal of the clubs, the investors will not invest in the club and the club may be loss a lot of money that they should be. So, we have developed the web system for a football match result prediction method in order to help making investment decisions for investors and generating the guidance for developing their football teams. The objective of this project is to predict the football match results for the English Premier League, and to analyze factors affecting the outcome of the match for guiding team improvement. This project collected previous three-season match information from www.premierleague.com to predict the current league season match results. All collected data were analyzed by the machine learning technique for building a football match result prediction model, and for finding factors affecting on football match results to give advice for improving their football teams. The testing results of the prediction are shown that the accuracy and the precision are more than 70%. Therefore, this system can help the user get the guidance for improving the football team and the precise prediction of football match results.

Also see: Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football

Video"Mathematics for sports prediction: Machine Learning With Python Sports Prediction"

Machine learning (ML) is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. One of the expanding areas necessitating good predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In addition, club managers and owners are striving for classification models so that they can understand and formulate strategies needed to win matches. These models are based on numerous factors involved in the games, such as the results of historical matches, player performance indicators, and opposition information. This paper provides a critical analysis of the literature in ML, focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so, we identify the learning methodologies utilised, data sources, appropriate means of model evaluation, and specific challenges of predicting sport results. This then leads us to propose a novel sport prediction framework through which ML can be used as a learning strategy. Our research will hopefully be informative and of use to those performing future research in this application area.

Also see: Best Football Prediction Site

Random Forest Classifier for Sports Betting: https://zcodesystem.com/ 
                                                                                                       - ZCode™ Technology 

References

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8. Premierleague.com. (2019). Premier League Football News, Fixtures, Scores & Results. https://www.premierleague.com.
9. SourceTree. 2019. SourceTree | Free Git GUI for Mac and Windows. https://www.sourcetreeapp. com/.
10. Scikit-learn. 2019. Scikit-learn: machine learning in Python. https://github.com/scikit-learn/scikit-learn.
11. RapidMiner. 2019. RapidMiner Studio. https://rapidminer.com/.