- 8 (Registered)
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?
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.
I’m going to teach you how to calculate each of them – and give you the automated Excel workbooks with the full calculations in them so you can use them with your own data.
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.
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!
At the end of this course you will receive a certificate of completion. Post it to Facebook, your LinkedIn page or print it out and stick it on your wall. Just don’t throw darts at it, ok…
- Learn about the errors of regression models…
- …and the errors of classification models
- Learn how to calculate them…
- …use them on your own data…
- …and interpret them correctly – every time!
- Worked examples with real data
- Automated Excel workbooks – ready to use with your own data
Section 1: Introduction
Section 2: Errors of Regression Models
- Lecture 2.1 Introduction to the Errors of Regression-Based Predictive Models – Video
- Lecture 2.2 Errors, Residuals, Deviations – Video
- Lecture 2.3 Residuals – Video
- Lecture 2.4 Residuals – Downloadables
- Lecture 2.5 Practice Session
- Lecture 2.6 R-Squared – Video
- Lecture 2.7 R-Squared – Downloadables
- Lecture 2.8 Practice Session
- Lecture 2.9 Variance – Video
- Lecture 2.10 Variance – Downloadables
- Lecture 2.11 Practice Session
- Lecture 2.12 Mean Absolute Error – Video
- Lecture 2.13 Mean Absolute Error – Downloadables
- Lecture 2.14 Practice Session
- Lecture 2.15 Mean Error – Video
- Lecture 2.16 Mean Error – Downloadables
- Lecture 2.17 Practice Session
- Lecture 2.18 RMSE – Video
- Lecture 2.19 RMSE – Downloadables
- Lecture 2.20 Practice Session
- Lecture 2.21 Bias/Variance Trade-Off – Video
- Lecture 2.22 Recommendations – Video
- Lecture 2.23 Recommendations – Downloadables
- Lecture 2.24 Practice Session
Section 3: Errors of Classification Models
- Lecture 3.1 Introduction to the Errors of Classification-Based Predictive Models – Video
- Lecture 3.2 Confusion Matrix – Video
- Lecture 3.3 Confusion Matrix – Downloadables
- Lecture 3.4 Practice Session
- Lecture 3.5 PPV & NPV – Video
- Lecture 3.6 PPV & NPV – Downloadables
- Lecture 3.7 Practice Session
- Lecture 3.8 Sensitivity & Specificity – Video
- Lecture 3.9 Sensitivity & Specificity – Downloadables
- Lecture 3.10 Practice Session
- Lecture 3.11 Accuracy – Video
- Lecture 3.12 Accuracy – Downloadables
- Lecture 3.13 Practice Session
- Lecture 3.14 F-measure & Youden’s J – Video
- Lecture 3.15 F-measure & Youden’s J – Downloadables
- Lecture 3.16 Practice Session
- Lecture 3.17 ROC Curves – Video
- Lecture 3.18 ROC Curves – Downloadables
- Lecture 3.19 Practice Session
- Lecture 3.20 AUC & Gini Coefficient – Video
- Lecture 3.21 AUC & Gini Coefficient – Downloadables
- Lecture 3.22 Practice Session
- Lecture 3.23 Multi-Class Classifications – Video
- Lecture 3.24 Recommendations – Video
- Lecture 3.25 Recommendations – Downloadables
- Lecture 3.26 Practice Session
Section 4: Course Recap
- Over 2.5 hours on-demand video
- Language: English
- Certificate of Completion