How and Why to Interpret Black Box Models
How to Pick a Winning March Madness Bracket
Updating a Data Pipeline with AWS’s Latest Offerings
Improve Predictive Model Performance With Ensembles
Dr. Jordan Barr takes explores the attributes and applications of model ensembles and potential downsides to provide context for when to use them.
Modeling Outcomes: Explain or Predict
This blog by Peter Bruce explores the differences between modeling for description versus for explanation and how your goals determine which method to use.
Ways Machine Learning Models Fail: Missing Causes
Transaction Classification Aids Credit Risk Assessment
Data Engineering with Discipline
When trying to get decision-making insights from data, we often must start with helping to clean and organize the data architecture so we can build data science and machine learning models, a process called data engineering. This blog explores process of preparing data for analytical analysis.
The Problem with Random Stratified Partitioning
Group Optimization – An Application of the Nash Equilibrium
Supervised vs. Unsupervised Machine Learning
How to Automate Machine Learning Model Tuning
Hyperparameters are the high-level “knobs” or “levers” of a model. In this blog Data Scientist Dr. Trent Bradberry explores hyperparameters in more detail and some ways to find good sets of them to reliably automate the model tuning process.
Fluency in The Language of Data Models
The Power of Open Data and Crowdsourcing Analytics
What is Data Wrangling and Why Does it Take So Long?
Credit Models are Winning and I’m Keeping Score!
What is a Data Detective? How to go Deeper With Your Data
Jump Start Your Modeling with Random Forests
Ensembles & Regularization – Analytics Superheros
Analytics Help Identify the Early Stages of a Stroke