# Tag: Logistic Regression (30)

**Linear to Logistic Regression, Explained Step by Step**- Mar 3, 2020.

Logistic Regression is a core supervised learning technique for solving classification problems. This article goes beyond its simple code to first understand the concepts behind the approach, and how it all emerges from the more basic technique of Linear Regression.**How to Build Your Own Logistic Regression Model in Python**- Oct 31, 2019.

A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.**Common Machine Learning Obstacles**- Sep 9, 2019.

In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.**A Gentle Introduction to Noise Contrastive Estimation**- Jul 25, 2019.

Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.**KDnuggets™ News 19:n05, Jan 30: Your AI skills are worth less than you think; 7 Steps to Mastering Basic Machine Learning**- Jan 30, 2019.

Also: Logistic Regression: A Concise Technical Overview; AI is a Big Fat Lie; How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy; Airbnb Rental Listings Dataset Mining; Data Science Project Flow for Startups**Logistic Regression: A Concise Technical Overview**- Jan 23, 2019.

Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.**Supervised Learning: Model Popularity from Past to Present**- Dec 28, 2018.

An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?**Multi-Class Text Classification with Doc2Vec & Logistic Regression**- Nov 9, 2018.

Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.**5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist**- May 8, 2018.

Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.**KDnuggets™ News 18:n08, Feb 21: Neural network AI is simple – stop pretending you are a genius; Data Science at the command line**- Feb 21, 2018.

Want a Job in Data? Learn This; 5 Things You Need To Know About Data Science; Cartoon: Machine Learning Problems in 2118**Logistic Regression: A Concise Technical Overview**- Feb 16, 2018.

Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.**Machine Learning Model Metrics**- Jan 23, 2018.

In this article we explore how to calculate machine learning model metrics, using the example of fraud detection. We'll see lots of different ways that we can try to understand just how good our learned model is.**7 Steps to Mastering Deep Learning with Keras**- Oct 30, 2017.

Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.**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.