Errors of Predictive Models
You’ve created a bunch of predictive models and you need to measure the accuracies of each of them to choose the one that’s best for your purposes.
In this course I’m going to show you all of the most used measures and teach you exactly when to use them – and when not to.
Most importantly I’m going to show you how to interpret each of them, so you always know which is the best measure for your uses.
Video lessons
Practice sessions
Downloadable resources
Certificate of completion
Interactive experience
Perfect for beginners
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So you’ve created a bunch of predictive models and now you need to choose the one that’s best for your purposes.
How do you do this? Eeny meeny? Throw darts at them?
Probably not.
You need to be able to measure the accuracies of each of your models so you can choose between them objectively.
The only problem is that there are literally loads of different measures you can use. Reading around the internet and watching videos on YouTube will only make you more confused, because each method has its own set of dedicated fans.
In this course I’m going to show you all of the most used measures and show you exactly when to use them and – more importantly – when not to.
And there’s only one place in the universe that you can get this course – right here!
If you’re truly interested in measuring the effectiveness of your models, don’t miss this course!
Here's what you'll learn:
you’ll learn why most papers textbooks and articles get the terminology wrong – and how to get it right
you’ll learn how to calculate the errors of regression-based predictive models – with lots of practice sessions
you’ll learn how to calculate the errors of classification-based predictive models –lots more practice sessions
you’ll learn exactly which statistical tests to use, why, when and how – and how to interpret them correctly too!
Your Curriculum
1
Introduction to the Errors of Predictive Models
An introduction to this course and what to expect as you learn from the video lessons and practice exercises
Open Access
Introduction to the course
2
Errors of Regression Models (Part 1)
In this chapter you'll learn the building blocks of calculating the variability of your data using R-Squared and Variance
Open Access
Free Plan
Introduction to the Errors of Regression-Based Predictive Models
Errors, Residuals, Deviations
Residuals
R-Squared
Variance
3
Errors of Regression Models (Part 2)
In this chapter you'll learn the building blocks of calculating the bias of your data, why how and when to use each measure
Free Plan
Premium
Mean Absolute Error
Mean Error
RMSE
Bias/Variance Trade-Off
Recommendations
4
Errors of Classification Models (Part 1)
In this chapter you'll learn how to measure the accuracy of your models - and why you need additional measures!
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Introduction to the Errors of Classification-Based Predictive Models
Confusion Matrix
PPV & NPV
Sensitivity & Specificity
Accuracy
5
Errors of Classification Models (Part 2)
In this chapter you'll learn about the additional measures you need, how to calculate them, when and why!
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F-measure & Youden’s J
ROC Curves
AUC & Gini Coefficient
Multi-Class Classifications
Recommendations
6
Course Recap
In this chapter you'll recap everything you've learnt about assessing regression-based and classification-based predictive models
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Course Recap
Your Course Certificate
