# Predictive Modeling (24)

**A $9B AI Failure, Examined**- Dec 7, 2021.

What happened at Zillow? An important real-world lesson in... just because you have a cool AI tool, doesn't mean that alone becomes your business model.**How to Evaluate the Performance of Your Machine Learning Model**- Sep 3, 2020.

You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.**The Machine Learning Field Guide**- Aug 3, 2020.

This straightforward guide offers a structured overview of all machine learning prerequisites needed to start working on your project, including the complete data pipeline from importing and cleaning data to modelling and production.**Evidence Counterfactuals for explaining predictive models on Big Data**- May 18, 2020.

Big Data generated by people -- such as, social media posts, mobile phone GPS locations, and browsing history -- provide enormous prediction value for AI systems. However, explaining how these models predict with the data remains challenging. This interesting explanation approach considers how a model would behave if it didn't have the original set of data to work with.**Idiot’s Guide to Precision, Recall, and Confusion Matrix**- Jan 13, 2020.

Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough.**ModelOps – Get it done. 3 Day Webinar Mini-Series**- Feb 15, 2019.

Join us for an educational series ModelOps - Get it done. Learn how a combination of technology and processes can help solve modelOps.**Using Confusion Matrices to Quantify the Cost of Being Wrong**- Oct 11, 2018.

The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.**Using Linear Regression for Predictive Modeling in R**- Jun 1, 2018.

In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.**Time Series for Dummies – The 3 Step Process**- Mar 5, 2018.

Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.**Join CommBank to push the boundaries of what is, and what could be**- Mar 30, 2017.

CommBank, Australia leading bank, is searching for smarter, faster and better solutions. Which is why we're investing in people like you. Talented analytics professionals ready for the next step in their career.**Getting Started with Data Science – R**- Aug 3, 2016.

A great introductory post from DataRobot on getting started with data science in R, including cleaning data and performing predictive modeling.**Getting Started with Data Science – Python**- Aug 1, 2016.

A great introductory post from DataRobot on getting started with data science in the Python ecosystem, including cleaning data and performing predictive modeling.**Workshop opportunities – Hadoop, R, Predictive Modeling**- Apr 11, 2016.

Spots are limited for the upcoming training workshops at Predictive Analytics World for Business, June 20-23, 2016 in Chicago. Check the new Hadoop workshop and instruction about advanced methods, modeling methods, R, and more – reserve your spot today.**Peering into the Black Box and Explainability**- Feb 2, 2016.

In many domains, where data science can be a game changer, and the biggest hurdle is not collecting data or building the models, it is Understanding what they mean.**The Data Science Conference 2015 Highlights**- Nov 18, 2015.

Here are the highlights from The Data Science Conference 2015, Nov 12-13 at University of Chicago. A two-day conference on Data Science, big data, machine learning, artificial intelligence & predictive modeling discussions -"for professionals" by professionals.**Marketing Strategies for Retail Customers Based on Predictive Behavior Models**- Nov 6, 2015.

Get a look at how a leading financial services provider used predictive analytics to deliver an effective direct marketing approach - download slides now.**Interview: Ranjan Sinha, eBay on Advanced Hadoop Cluster Management through Predictive Modeling**- Jun 9, 2015.

We discuss categorization of e-commerce analytics, opportunities/ challenges of Big Data, Astro predictive model for Hadoop cluster management, and Apache Kylin.**Automatic Statistician is here: Dr. Mo**- Mar 21, 2015.

Dr. Mo, Automatic Statistician is here! Using Artificial Intelligence, self-learning algorithm, multimodel technology Dr. Mo achieves Super Accuracy and Speed. Simple use and simple output for non-statisticians.**Interview: Anthony Bak, Ayasdi on Managing Data Complexity through Topology**- Jan 28, 2015.

We discuss the definition of Topology, its relevance to Big Data and compare Topological Data Analysis (TDA) with other approaches.**Learning Data Science and Predictive Modeling at Your Own Pace – A Free Online Video Series**- Jan 8, 2015.

A twenty part video training series on Predictive Modeling, offering the Rapid Insight and the concepts behind the predictive models.**HR & Workforce Analytics Innovation Summit 2014 Chicago: Day 1 Highlights**- Jun 2, 2014.

Highlights from the presentations by HR leaders from Wells Fargo, Sears Holdings, Johnson Controls, Trulia on day 1 of HR & Workforce Analytics Innovation Summit 2014 in Chicago.**Code for India 2014 Global Hack-a-thon – Building a Better India through Innovative Solutions**- May 19, 2014.

Non-stop 24 hours of coding at the Code for India 2014 hackathon leads to creative solutions for major social problems of India through interesting software applications.**The Data Mining Group releases PMML v4.2 Predictive Modeling Standard**- Feb 25, 2014.

The Data Mining Group, a vendor-led consortium of companies and organizations developing standards for statistical and data mining models, announced the general availability of version 4.2 of the Predictive Model Markup Language (PMML).**Data Mining for Beginners Boot Camp, Salford video series**- Jan 29, 2014.

This series shows how to easily apply SPM software suite to your predictive modeling projects, using a modern banking application as an example. This series is at the beginner level, and is perfect for first-time users or for those who need a refresher course in model building and data analysis.