Set classification aims to classify a set of observations as a whole, as opposed to classifying individual observations separately. To formally understand the unfamiliar concept of binary set classification, we first investigate the optimal decision rule under the normal distribution, which utilizes the empirical covariance of the set to be classified. We show that the number of observations in the set plays a critical role in bounding the Bayes risk. Under this framework, we further propose new methods of set classification. For the case where only a few parameters of the model drive the difference between two classes, we propose a computationally-efficient approach to parameter estimation using linear programming, leading to the Covariance-engaged LInear Programming Set (CLIPS) classifier. Its theoretical properties are investigated for both independent case and various (short-range and long-range dependent) time series structures among observations within each set. The convergence rates of estimation errors and risk of the CLIPS classifier are established to show that having multiple observations in a set leads to faster convergence rates, compared to the standard classification situation in which there is only one observation in the set. The applicable domains in which the CLIPS performs better than competitors are highlighted in a comprehensive simulation study. Finally, we illustrate the usefulness of the proposed methods in classification of real image data in histopathology.

An important issue in clustering concerns the avoidance of false positives while searching for clusters. This work addressed this problem considering agglomerative methods, namely single, average, median, complete, centroid and Ward's approaches applied to unimodal and bimodal datasets obeying uniform, gaussian, exponential and power-law distributions. A model of clusters was also adopted, involving a higher density nucleus surrounded by a transition, followed by outliers. This paved the way to defining an objective means for identifying the clusters from dendrograms. The adopted model also allowed the relevance of the clusters to be quantified in terms of the height of their subtrees. The obtained results include the verification that many methods detect two clusters in unimodal data. The single-linkage method was found to be more resilient to false positives. Also, several methods detected clusters not corresponding directly to the nucleus. The possibility of identifying the type of distribution was also investigated.

Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data. While the effects on model accuracy of such poisoning attacks have been widely studied, their potential effects on other model performance metrics remain to be evaluated. In this work, we introduce an optimization framework for poisoning attacks against algorithmic fairness, and develop a gradient-based poisoning attack aimed at introducing classification disparities among different groups in the data. We empirically show that our attack is effective not only in the white-box setting, in which the attacker has full access to the target model, but also in a more challenging black-box scenario in which the attacks are optimized against a substitute model and then transferred to the target model. We believe that our findings pave the way towards the definition of an entirely novel set of adversarial attacks targeting algorithmic fairness in different scenarios, and that investigating such vulnerabilities will help design more robust algorithms and countermeasures in the future.

Outlier Detection is also known as anomaly detection, noise detection, deviation detection, or exception mining. There is no universally accepted definition. An early definition by (Grubbs, 1969) is: An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs. An observation which appears to be inconsistent with the remainder of that set of data. A list of applications that utilize outlier detection according to (Hodge, V.J. and Austin, J., 2014) is: This is analogous to unsupervised clustering.

In this article, we focus on'Time Series Data' which is a part of Sequence models. In this article, we focus on'Time Series Data' which is a part of Sequence models. In essence, this represents a type of data that changes over time such as the weather of a particular place, the trend of behaviour of a group of people, the rate of change of data, the movement of body in a 2D or 3D space or the closing price for a particular stock in the markets. Analysis of time series data can be done for anything that has a'time' factor involved in it. So what can machine learning help us achieve over time series data? It can also be used to predict missing values in the data. There are certain keywords that always come up when dealing with time series data.

Creating a great machine learning system is an art. There are a lot of things to consider while building a great machine learning system. But often it happens that we as data scientists only worry about certain parts of the project. Most of the time that happens to be modeling, but in reality, the success or failure of a Machine Learning project depends on a lot of other factors. It is essential to understand what happens before training a model and after training the model and deploying it in production.

Online Courses Udemy Deep Learning Prerequisites: Logistic Regression in Python, Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Natural Language Processing with Deep Learning in Python Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Unsupervised Machine Learning Hidden Markov Models in Python Modern Deep Learning in Python Preview this course GET COUPON CODE Description This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

XGBoost is a popular Python library for gradient boosted decision trees. The implementation allows practitioners to distribute training across multiple compute instances (or workers), which is especially useful for large training sets. One tool used at Zalando for deploying production machine learning models is the managed service from Amazon called SageMaker. XGBoost is already included in SageMaker as a built-in algorithm, meaning that a prebuilt docker container is available. This container also supports distributed training, making it easy to scale training jobs across many instances.

Linear regression, also known as simple linear regression or bivariate linear regression, is used when we want to predict the value of a dependent variable based on the value of an independent variable. Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way.

Welcome to another blog on Logistic regression in python. In previous blog Logistic Regression for Machine Learning using Python, we saw univariate logistics regression. And we saw basic concepts on Binary classification, Sigmoid Curve, Likelihood function, and Odds and log odds. In, this section first will take a look at Multivariate Logistic regression concepts. And then we will be building a logistic regression in python.