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Module 7: Assessment and Measurements

This module will teach you how to appropriately assess your model. We will teach you how to evaluate and calculate your model using an assortment of different measurements.

0Module Learning Outcomes

1Introducing Evaluation Metrics

2Name That Value!

3True or False: Confusion Matrix

4Code a Confusion Matrix

5Precision, Recall and F1 Score

6Let's Calculate

7True or False: Measurements

8Using Sklearn to Obtain Different Measurements

9Multi-Class Measurements

10Using Sklearn to Obtain Different Measurements

11Multi-class Questions

12True or False: Measurements

13Imbalanced Datasets

14True or False: Unbalanced Data

15Balancing our Data in Action

16Regression Measurements

17Name That Measurement!

18True or False: Regression Measurements

19Calculating "by hand"

20Calculating Regression Measurements

21Passing Different Scoring Methods

22True or False: Scoring with Cross-Validation

23Scoring and Cross Validation

24What Did We Just Learn?

About this course

This course covers the data science perspective on the introductory concepts in machine learning, with a focus on making predictions. It covers how to build different models such as K-NN, decision trees and linear classifiers as well as important concepts such as data splitting and fundamental rules and laws. In addition, this course will teach you how to evaluate models properly and question their validity all while streamlining the process with pipelines.

About the program

The University of British Columbia (UBC) is a comprehensive research-intensive university, consistently ranked among the 40 best universities in the world. The Key Capabilities in Data Science program was launched in September 2020 and is developed and taught by many of the same instructors as the UBC Master of Data Science program.