A Gentle Introduction to Imbalanced Classification

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Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. The distribution can vary from a slight bias to a severe imbalance where there is one example in the minority class for hundreds, thousands, or millions of examples in the majority class or classes. Imbalanced classifications pose a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. This results in models that have poor predictive performance, specifically for the minority class.


Linear Discriminant Analysis for Machine Learning - Machine Learning Mastery

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Logistic regression is a classification algorithm traditionally limited to only two-class classification problems. If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique. In this post you will discover the Linear Discriminant Analysis (LDA) algorithm for classification predictive modeling problems. This post is intended for developers interested in applied machine learning, how the models work and how to use them well. As such no background in statistics or linear algebra is required, although it does help if you know about the mean and variance of a distribution.


Linear Discriminant Analysis for Machine Learning

#artificialintelligence

Logistic regression is a classification algorithm traditionally limited to only two-class classification problems. If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique. In this post you will discover the Linear Discriminant Analysis (LDA) algorithm for classification predictive modeling problems. This post is intended for developers interested in applied machine learning, how the models work and how to use them well. As such no background in statistics or linear algebra is required, although it does help if you know about the mean and variance of a distribution.


Extracting Predictive Statements with Their Scope from News Articles

AAAI Conferences

We estimate that a large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this paper, we approach the problem of automatically extracting future-related information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence.


Predictive Analytics with TensorFlow

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Predictive decisions are becoming a huge trend worldwide catering wide sectors of industries by predicting which decisions are more likely to give maximum results. The data mining, statistics, machine learning allows users to discover predictive intelligence by uncovering patterns and showing the relationship among the structured and unstructured data. This book will help you build solutions which will make automated decisions. In the end tune and build your own predictive analytics model with the help of TensorFlow. This book will be divided in three main sections.