# Tag: Logistic Regression (17)

**Top 10 Machine Learning Algorithms for Beginners**- Oct 20, 2017.

A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.**Learn Generalized Linear Models (GLM) using R**- Oct 11, 2017.

In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.**Getting Up Close and Personal with Algorithms**- Mar 21, 2017.

We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.**The Evolution of Classification, Oct 19, Oct 26 Webinars**- Oct 7, 2016.

Join us for this two part webinar series on the Evolution of Classification, presented by Senior Scientist, Mikhail Golovnya.**Top Data Scientist Claudia Perlich’s Favorite Machine Learning Algorithm**- Sep 27, 2016.

Interested in the reasons why a top data scientist is partial to one particular algorithm over others? Read on to find out.**What is the Role of the Activation Function in a Neural Network?**- Aug 30, 2016.

Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.**A Primer on Logistic Regression – Part I**- Aug 24, 2016.

Gain an understanding of logistic regression - what it is, and when and how to use it - in this post.**KDnuggets™ News 16:n24, Jul 6: Text Mining 101; Softmax and Logistic Regression; Data Mining History: Support Vector Machines**- Jul 6, 2016.

What is Softmax Regression and How is it Related to Logistic Regression; Text Mining 101: Topic Modeling; Data Mining History: The Invention of Support Vector Machines; Mining Twitter Data with Python Part 5: Data Visualisation Basics**What is Softmax Regression and How is it Related to Logistic Regression?**- Jul 1, 2016.

An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.**Regularization in Logistic Regression: Better Fit and Better Generalization?**- Jun 24, 2016.

A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.**The Development of Classification as a Learning Machine**- Apr 29, 2016.

An explanation of how classification developed as a learning machine, from LDA to the perceptron, on to logistic regression, and through to support vector machines.**Using Ensembles in Kaggle Data Science Competitions- Part 3**- Jun 27, 2015.

Earlier, we showed how to create stacked ensembles with stacked generalization and out-of-fold predictions. Now we'll learn how to implement various stacking techniques.**Upcoming Webcasts on Analytics, Big Data, Data Science – Jun 23 and beyond**- Jun 22, 2015.

Which Data Should You Move to Hadoop, Using Data from Hadoop to Improve Your Business, Tips and Tricks for Logistic Regression, Data Mining: Failure to Launch, and more.**Webinar: Tips & Tricks to Improve Your Logistic Regression, June 25**- Jun 10, 2015.

Learn more advanced and intuitive machine learning techniques that improve on standard logistic regression in accuracy and other aspects. A step-by-step presentation that you can repeat on your own.**Upcoming Webcasts on Analytics, Big Data, Data Science – Jun 2 and beyond**- Jun 1, 2015.

Data Mining - Failure to Launch, Improve Customer Experience Management with Text Analytics, Why and When to Embed Business Intelligence, Tips & Tricks for Logistic Regression, and more.**Cloud Machine Learning Wars: Amazon vs IBM Watson vs Microsoft Azure**- Apr 16, 2015.

Amazon recently announced Amazon Machine Learning, a cloud machine learning solution for Amazon Web Services. Able to pull data effortlessly from RDS, S3 and Redshift, the product could pose a significant threat to Microsoft Azure ML and IBM Watson Analytics.**Identity Fraud and Analytics – An Overview**- Mar 26, 2014.

With the consumers being increasingly concerned about identity theft, leading financial institutions are leveraging analytics to detect Identity Fraud as it happens.