Logistic Regression: A Concise Technical Overview

#artificialintelligence

We have all heard of Linear Regression. It's what we all learn in our first semester of statistics. It is our default technique when we have a continuous outcome variable. A refresher on Linear Regression can be found here. But other times we have Categorical Outcomes.


Choosing the Correct Type of Regression Analysis

@machinelearnbot

There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. In this post, I cover the more common types of regression analyses and how to decide which one is right for your data. I'll provide an overview along with information to help you choose. I organize the types of regression by the different kinds of dependent variable.


Encoding Variables: Translating Your Data so the Computer Understands It

#artificialintelligence

Humans and computers don't understand data in the same way, and an active area of research in AI is determining how AI "thinks" about data. For example, the recent Quanta article Where We See Shapes, AI Sees Textures discusses an inherent disconnect between how humans and computer vision AI interpret images. The article addresses the implicit assumption many people have that when AI works with an image, it interprets the contents of the image the same way people do- by identifying the shapes of the objects. However, because most AI interprets images at a pixel level, it is more intuitive for the AI to label images by texture (i.e., more pixels in an image represent an object's texture than an object's outline or border) than by shape. Another useful example of this is in language.


Develop a Model for the Imbalanced Classification of Good and Bad Credit - AnalyticsWeek

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


Develop a Model for the Imbalanced Classification of Good and Bad Credit

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.