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)

As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns. Its decision boundary was drawn almost perfectly parallel to the assumed true boundary, i.e. Awful result, it appears to never follow the true boundary. Just a little improved, but it still appears to be overfitted. Even worse... it appears to get more overfitted than the case of 2-classes.

I use the methodology you speak of all the time. I was the original programer for Breiman and Stone's version of CART in the late 70's which is where I believe I was first introduced to that method. However we were very careful to use the term variation explained since there is little relationship to the theoretical Pearson "r". Be aware that this value can go negative. Which implies that parts of your model behave a lot higher variation then the population variance.

This article is by Jitesh Shah, a data & stats jockey in perpetual beta, located in Fremont, California. This article includes the data set and Python code. Wouldn't it be nice if defects and product failures can be predicted in advance. We've got the data on attributes and design features and manufacturing processes that come together and creates that product and we have defect and failure rate data so all we got to do is connect the two and use that to predict which set of features and attributes and processes in combination cause these defects. That was probably a non-trivial endeavor in the past but now with the ability to store and process vast amounts of data (no secret there), no big deal.

Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced set, which is usually measured upon patient admittance. Both models produced good results, with a prediction accuracy of 0.88 and 0.86, when considering the list of five most probable diseases, and 0.59 and 0.57, when considering only the most probable disease. Models did not differ significantly from each other, which indicates that a reduced set of parameters contains a relevant fingerprint of a disease, expanding the utility of the model for general practitioner's use and indicating that there is more information in the blood test results than physicians recognize. In the clinical test we showed that the accuracy of our predictive models was on a par with the ability of hematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis.

The lending ecosystem around the world has been at the centre of significant changes in the last decade. From financial technology disrupting the financial services sector industry with highly efficient and cost-effective processes, to stringent regulations following the 2008 global financial crisis, the growing technological intervention has played a significant role in the rapid evolution of the lending industry. One such technology is machine learning which has begun to create new and highly promising avenues in the lending market. Machine learning is a Predictive Model Algorithm that develops Artificial Intelligence around large sets of data through different predictive statistical techniques (such as Logistic Regression, Random Forest, Decision Tree etc.) and imparts decisions/insights based on the data it processes. Machines can be taught to identify any form of data which is stored electronically such as texts, images, speech, etc. and analyse by the machine through such algorithms to identify behaviours, patterns etc. and generate similar predictions when imposed on a new dataset. Fintech companies are increasingly augmenting the applications of machine learning algorithms in their operations to build efficient and effective systems.

Causality has been recently introduced in databases, to model, characterize, and possibly compute causes for query answers. Connections between QA-causality and consistency-based diagnosis and database repairs (wrt. integrity constraint violations) have already been established. In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality. We also obtain new results on the complexity of view-conditioned causality, and investigate the notion of QA-causality in the presence of integrity constraints, obtaining complexity results from a connection with view-conditioned causality. The abduction connection under integrity constraints allows us to obtain algorithmic tools for QA-causality.

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. Decision trees work with simple "if" clauses dichotomically chained together, splitting the data flow recursively on those "if"s until they reach a leaf where we can categorize the data. Such data inspection could be used to reverse engineer the behavior of any function. 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.

Machine learning is a problem of trade-offs. The classic issue is overfitting versus underfitting. Overfitting happens when a model memorizes its training data so well that it is learning noise on top of the signal. Underfitting is the opposite: the model is too simple to find the patterns in the data. Simplicity versus accuracy is a similar consideration. Do you want a model that can fit onto one sheet of paper and be understood by a broad audience?