Decision Tree Learning


Discover structure behind data with decision trees - Vooban

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Let's understand and model the hidden structure behind data with Decision Trees. In this tutorial, we'll explore and inspect how a model can do its decisions on a car evaluation data set. Since decision trees are good algorithms for discovering the structure hidden behind data, we'll use and model the car evaluation data set, for which the prediction problem is a (deterministic) surjective function. The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety.


A Practical Guide to Tree Based Learning Algorithms

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Common examples of tree based models are: decision trees, random forest, and boosted trees. CART model involves selecting input variables and split points on those variables until a suitable tree is constructed. In order to perform recursive binary splitting, first select the predictor $X_j$ and the cut point $s$ such that splitting the predictor space into the regions (half planes) \(R_1(j,s) \big\{ X Xj s \big\}\) and \(R_2(j,s) \big\{ X Xj \ge s \big\}\) leads to the greatest possible reduction in RSS. Just as in the regression setting, recursive binary splitting is used to grow a classification tree.


Displayr Machine Learning: Pruning Decision Trees

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In non-technical terms, it works by repeatedly finding the best predictor variable to split the data into two subsets. The CART algorithm will repeatedly partition data into smaller and smaller subsets until those final subsets are homogeneous in terms of the outcome variable. In this case early stopping produces such a simple simple tree that pruning has no effect. The "sweet spot" in the middle is without early stopping, and pruning to the minimum cross validation error.


Two-Class Boosted Decision Tree

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Two-Class Boosted Decision Tree module creates a machine learning model that is based on the boosted decision trees algorithm. A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Step 3 For the maximum number of leaves per tree, indicate the maximum number of terminal nodes (leaves) that can be created in any tree. Step 4 For the minimum number of samples per leaf node, indicate the number of cases required to create any terminal node (leaf) in a tree.


Super Intelligence for The Stock Market – Numerai – Medium

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At Numerai, we're ensembling machine intelligence from thousands of data scientists around the world to achieve breakthroughs in stock market prediction accuracy. With many different solutions to the same problem, Numerai is able to combine each model into a meta model just like Random Forests combines decision trees into a forest. Numerai's meta model gains simultaneous exposures to every model meaning our hedge fund holds many more independent bets than a portfolio built from just one model. Ensembling many diverse models permits lower error rates in machine learning, higher returns on individual trades, lower portfolio volatility, and higher portfolio exposure.


Machine Learning for Everyone

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So basically, a model performs mapping between inputs and an output, finding-mysteriously, sometimes-the relationships between the input variables in order to predict any other variable. It's about becoming familiar with one of the most-used predictive models: Random Forest (official algorithm site), implemented in R, one of the most-used models due to its simplicity in tuning and robustness across many different types of data. So the final decision produced by the random forest model is the result of voting by all the decision trees. You're already familiar with decision tree outputs: they produce IF-THEN rules, such as, If the user has more than five visits, he or she will probably use the app.


Machine Learning: A Visual Guide to Machine Learning with Python, Data Science, TensorFlow, Artificial Intelligence, Random Forests and Decision Trees

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Machine learning is a type of artificial intelligence program that you can use to give your computer the ability to learn without being completely programmed. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Machine learning focuses deeply on developing computer programs that can change when exposed to new data. In addition to that, ML studies the construction of algorithms and how to make predictions on data.


The Artificial 'Artificial Intelligence' Bubble and the Future of Cybersecurity

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I think the recent article in the New York Times about the boom in'artificial intelligence' in Silicon Valley made many people think hard about the future of cybersecurity – both the near and distant future. Er, so, like, why would any venture capitalist worth his silicon invest in an AI-venture? Instead, they want to create bubbles: attracting investors, selling up quickly with a share price based on the'valuation of future profit', and then… well, then the mess (massive losses suffered) will already be someone else's problem. For example, we've introduced boosting and decision tree learning technologies for detecting sophisticated targeted attacks and proactive protection against future threats (yep – threats that don't exist yet!).


Simple Decision Tree Excel Add-in

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Simple Decision Tree is an Excel Add-in created by Thomas Seyller. The Add-in is released under the terms of GPL v3 with additional permissions. Thomas created this Add-in for the Stanford Decisions and Ethics Center and open-sourced it for the Decision Professionals Network. This software has been extensively used to teach Decision Analysis at Stanford University.


Implementing Decision Trees using Scikit-Learn – Prashant Gupta – Medium

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Scikit-Learn is a popular library for Machine Learning in python programming language. Decision Trees are a machine learning algorithm that can be used for both Classification and Regression. You can get started with implementing Decision Tree algorithms using scikit-learn, with very little knowledge about them. Scikit-Learn has a nice documentation of their API for learning a decision tree classifier from your data.