Detect and Reduce Sample Bias in Predictive Analytics
course's star rating by considering a number of different factors
such as the number of ratings, the age of ratings, and the
likelihood of fraudulent ratings.
Find online courses made by experts from around the world.
Take your courses with you and learn anywhere, anytime.
Learn and practice real-world skills and achieve your goals.
One of the most common mistakes an analyst could make during a predictive modeling project is ignorance of sample bias in their data. The cost of making such mistake can be quite substantial to an organization's business outcome.
In this 30-min video course, we will share with you some secrets on how to avoid this mistake. You will learn the following topics:
At the end of the class, you should have gained sufficient knowledge to help you detect and reduce sample bias in future predictive modeling or advanced analytics projects.
Not for you? No problem.
30 day money back guarantee.
Learn on the go.
Desktop, iOS and Android.
Certificate of completion.
|Section 1: Introduction and motivation|
Explain the concept of sample bias with an illustration
Identify major sources of sample bias in practice with a real-world example
Explain why we should be concerned with sample bias through a credit risk modeling example
Example of sample bias in credit risk modeling
Detailed example on how a sample biases can lead to poor business outcome
|Section 2: How to detect sample bias|
Discuss sample bias detection approach 1: relationship pattern analysis
Discuss sample bias detection approach 2: variable distribution analysis
|Section 3: How to reduce sample bias|
Discuss sample bias reduction approach 1: random sample test
Discuss sample bias reduction approach 1: bias reduction modeling
Discuss sample bias reduction approach 1: reject inference
|Section 4: Key learnings|
Discuss 3 key takeways from this course
15+ years of professional experience on predictive modeling in different industries. Specialized in logistic regression, linear regression, generalized regression, survival analysis, decision tree, discriminant analysis, cluster analysis, and other advanced analytic. In-depth knowledge and experiences in customer acquisition, retention, and credit risk management. Developed training classes and taught classes to graduate students and other working professionals.