Meaningful Predictive Modeling

Meaningful Predictive Modeling

Meaningful Predictive Modeling This course is part of Python Data Products for Predictive Analytics Specialization Taught in English 22 languages available Some content may not be translated Instructors: Julian McAuley +1 more Close Instructors Instructor ratings We asked all learners to give feedback on our instructors based on the quality of their teaching style. Close

SKU: zc1P9CrfqX

Description

Predictive modeling is a powerful tool that can help organizations make informed decisions based on past data and future probabilities. However, not all predictive models are created equal. In order for predictive modeling to be truly beneficial, it needs to be meaningful. Meaningful predictive modeling goes beyond just accurate predictions – it must provide actionable insights that can drive positive outcomes for businesses and individuals.

One key aspect of meaningful predictive modeling is the selection of relevant variables. In order to create an effective model, it is crucial to choose variables that are truly predictive of the outcome of interest. Including irrelevant or redundant variables can lead to overfitting, where the model performs well on the training data but fails to generalize to new, unseen data. By carefully selecting variables based on domain knowledge and statistical analysis, meaningful predictive models can provide valuable

Meaningful Predictive Modeling

This course is part of Python Data Products for Predictive Analytics Specialization

Taught in English

Some content may not be translated

Julian McAuley
Ilkay Altintas

Instructors: Julian McAuley

6,077 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.3

(47 reviews)

Intermediate level
Some related experience required
8 hours (approximately)
Flexible schedule
Learn at your own pace

What you’ll learn

  • Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).

  • Evaluate the performance of regressors / classifiers using the above measures.

  • Understand the difference between training/testing performance, and generalizability.

  • Understand techniques to avoid overfitting and achieve good generalization performance.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 quizzes

,

See how employees at top companies are mastering in-demand skills

About the author

The Editorial Team at Infolearners.com is dedicated to providing the best information on learning. From attaining a certificate in marketing to earning an MBA, we have all you need. If you feel lost, reach out to an admission officer.
Study on Scholarship Today -- Check your eligibility for up to 100% scholarship.