AUC & F Score - Metrics for Binary Classification Model
When you want to analyze what makes your customers convert, sign up, respond, etc. with data, building binary classification models with either machine learning or statistical learning algorithms would be a great tool to use.
When you build such models though, how would you know your models are good or bad?
That's where AUC and F Score come in. They are the two metrics that are used often to evaluate the prediction quality of the models.
In this seminar, Kan will discuss what AUC and F value are, why and when you need them, and how to use them in Exploratory.
Agenda:
- A Grammar of Analytics
- Accuracy Rate?
- Precision & Recall
- F1 Score
- AUC
Video
Here is the recorded video.
Learning Statistics Series
- Part 1 - Introduction to Hypothesis Testing - Link
- Part 2 - t-Test: Comparing Two Means - Link
- Part 3 - Kruskal Wallis Test: Comparing Several Means with No Assumption - Link
- Part 4 - Chi-Square Test - Are These Two Categorical Variables Related? - Link
- Part 5 - Correlation - 4 Ways to Discover & Investigate Correlation Effectively - Link
- Part 6 - Multivariate Analysis - How great do I perform without others? - Link
Past Seminars
You can check other past seminars on this page.
For the upcoming seminars, check out our Exploratory Online Seminar page!