Machine Learning : What & Why Part 2 – Towards Data Science

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Machine Learning(Revisited) is a Red hot cake in the intellectual & scientific world right now. Where the human intelligence is collaborating with this powerful machines to create some wonderful solutions for the problems which are futuristic and realistic too. The magical prediction this stream of AI can do is really mind-boggling . He gave the most simplified definition of Machine Learning and said " It's a field of study that gives computers the ability to learn without being explicitly programmed." There is one very famous theory called Bait Shyness where Rodents learn to avoid the foods which they feel will harm them.


Is there a Universal Classifier? One which can perform binary, multi-class and multi-label classification • /r/MachineLearning

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Machine learning classification can be categorized into single-label classification (binary and multi-class) and multi-label classification. Single label classification problems involve mapping each of the input vectors to its unique target class from a pool of target classes/labels. However, there are several classification problems in which the target classes are not mutually exclusive and the input samples belong to more than one target class. These problems cannot be classified using the single label classification thus resulting in the need for multi-label classification in which each input sample belongs to a subset of target classes. Several machine learning classifiers have been developed and is available in the literature for each of the classification types.


Data Preparation for Gradient Boosting with XGBoost in Python - Machine Learning Mastery

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XGBoost is a popular implementation of Gradient Boosting because of its speed and performance. Internally, XGBoost models represent all problems as a regression predictive modeling problem that only takes numerical values as input. If your data is in a different form, it must be prepared into the expected format. In this post you will discover how to prepare your data for using with gradient boosting with the XGBoost library in Python. Data Preparation for Gradient Boosting with XGBoost in Python Photo by Ed Dunens, some rights reserved.


Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars

arXiv.org Machine Learning

Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data describing 11 types of variable stars from the Catalina Real-Time Transient Surveys (CRTS), we illustrate how to capture the most important information from computed features and describe detailed methods of how to robustly use Information Theory for feature selection and evaluation. We apply three Machine Learning (ML) algorithms and demonstrate how to optimize these classifiers via cross-validation techniques. For the CRTS dataset, we find that the Random Forest (RF) classifier performs best in terms of balanced-accuracy and geometric means. We demonstrate substantially improved classification results by converting the multi-class problem into a binary classification task, achieving a balanced-accuracy rate of $\sim$99 per cent for the classification of ${\delta}$-Scuti and Anomalous Cepheids (ACEP). Additionally, we describe how classification performance can be improved via converting a 'flat-multi-class' problem into a hierarchical taxonomy. We develop a new hierarchical structure and propose a new set of classification features, enabling the accurate identification of subtypes of cepheids, RR Lyrae and eclipsing binary stars in CRTS data.


An Infinity-sample Theory for Multi-category Large Margin Classification

Neural Information Processing Systems

The purpose of this paper is to investigate infinity-sample properties of risk minimization based multi-category classification methods. These methods can be considered as natural extensions to binary large margin classification. We establish conditions that guarantee the infinity-sample consistency of classifiers obtained in the risk minimization framework. Examples are provided for two specific forms of the general formulation, which extend a number of known methods. Using these examples, we show that some risk minimization formulations can also be used to obtain conditionalprobability estimates for the underlying problem. Such conditional probability information will be useful for statistical inferencing tasksbeyond classification.