When modeling, it is important to clean the data sample to ensure that the observations best represent the problem. Sometimes a dataset can contain extreme values that are outside the range of what is expected and unlike the other data. These are called outliers and often machine learning modeling and model skill in general can be improved by understanding and even removing these outlier values. In this tutorial, you will discover outliers and how to identify and remove them from your machine learning dataset. Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code. How to Use Statistics to Identify Outliers in Data Photo by Jeff Richardson, some rights reserved.
The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling performance. Identifying and removing outliers is challenging with simple statistical methods for most machine learning datasets given the large number of input variables. Instead, automatic outlier detection methods can be used in the modeling pipeline and compared, just like other data preparation transforms that may be applied to the dataset. In this tutorial, you will discover how to use automatic outlier detection and removal to improve machine learning predictive modeling performance. Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code.
Welcome to the course "Complete Outlier Detection Algorithms A-Z: In Data Science". This is the most comprehensive, yet straight-forward, course for the outlier detection on UDEMY! Are you Data Scientist or Data Analyst or Financial Analyst or maybe you are interested in anomaly detection or fraud detection? The course is designed to teach you the various techniques which can be used to identify and recognize outliers in any set of data. The process of identifying outliers has many names in Data Science and Machine learning such as outlier modeling, novelty detection, or anomaly detection.
With global credit card fraud loss on the rise, it is important for banks, as well as e-commerce companies, to be able to detect fraudulent transactions (before they are completed). According to the Nilson Report, a publication covering the card and mobile payment industry, global card fraud losses amounted to $22.8 billion in 2016, an increase of 4.4% over 2015. This confirms the importance of the early detection of fraud in credit card transactions. Fraud detection in credit card transactions is a very wide and complex field. Over the years, a number of techniques have been proposed, mostly stemming from the anomaly detection branch of data science. In the first scenario, we can deal with the problem of fraud detection by using classic machine learning or statistics-based techniques. We can train a machine learning model or calculate some probabilities for the two classes (legitimate transactions and fraudulent transactions) and apply the model to new transactions so as to estimate their legitimacy.
Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. In order to overcome this limitation, such tasks are attempted as outlier or anomaly detection tasks. We also hypothesize that out- liers have behavioral patterns that change over time. Limited data and continuously changing patterns makes learning significantly difficult. In this work we are proposing an approach that detects outliers in large data sets by relying on data points that are consistent. The primary contribution of this work is that it will quickly help retrieve samples for both consistent and non-outlier data sets and is also mindful of new outlier patterns. No prior knowledge of each set is required to extract the samples. The method consists of two phases, in the first phase, consistent data points (non- outliers) are retrieved by an ensemble method of unsupervised clustering techniques and in the second phase a one class classifier trained on the consistent data point set is ap- plied on the remaining sample set to identify the outliers. The approach is tested on three publicly available data sets and the performance scores are competitive.