Model Evaluation and Selection Using scikit-learn

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Chetan Prabhu

1:17:03

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01 - Course Overview

01 - Course Overview.mp4

01:31

02 - What Is Model Evaluation and Selection#

02 - Model Evaluation and Selection.mp4

09:20

03 - Evaluation Methods for Classification Models

03 - Introduction.mp4

00:50

04 - Classification Model Refresher.mp4

01:19

05 - Confusion Matrix.mp4

02:10

06 - Accuracy, Precision, Recall, and F1 Score.mp4

03:40

07 - Choosing the Right Metric.mp4

04:30

08 - ROC Curves and AUC.mp4

03:41

09 - Demo.mp4

08:30

04 - Evaluation Methods for Regression Models

10 - Introduction.mp4

00:36

11 - Regression Model Refresher.mp4

01:32

12 - Mean Square Error and Root Mean Square Error.mp4

03:31

13 - Mean Absolute Error.mp4

01:04

14 - R-squared and Adjusted R-squared.mp4

05:19

15 - Choosing the Right Metric.mp4

02:20

16 - Demo.mp4

04:05

17 - Summary.mp4

00:34

05 - Model Selection Techniques

18 - Model Selection Techniques.mp4

13:25

06 - Putting It All Together

19 - Revisiting the Data Scientists Dilemma.mp4

01:51

20 - Model Evaluation Methods.mp4

01:56

21 - Model Selection Techniques.mp4

01:36

22 - Demo - Using the Patient Dataset.mp4

03:43

Description

Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.

What You'll Learn?

During the machine learning model building process, you will have to make some important decisions on how to evaluate how well your models perform, as well as how to select the best performing model. In this course, Model Evaluation and Selection Using scikit-learn, you will learn foundational knowledge/gain the ability to evaluate and select the best models. First, you will learn about a variety of metrics that you can use to evaluate how well your models are performing. Next, you will discover techniques for selecting the model that will perform the best in the future. Finally, you will explore how to implement this knowledge in Python, using the scikit-learn library. When you're finished with this course, you will have the skills and knowledge of needed to evaluate and select the best machine learning model from a set of models that you've built.

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Python

Artificial Intelligence

Chetan Prabhu

Instructor's Courses

Chetan is an accomplished data scientist who has worked across a variety of industries, including financial services, retail, advertising, and manufacturing. Most recently, he worked as a data scientist at Facebook, and is currently a Director of Data Science at United Technologies, where he is in a leadership role. Chetan has an undergraduate degree in engineering from Cooper Union, an MBA from Yale University, and a Masters degree in Statistics from Baruch College. He is a life-long learner, and regularly takes courses in mathematics, statistics, and computer science to further his understanding of modern data science techniques. Chetan is always eager to work with data to answer questions and provide insights. He is a true believer in data-driven decision making and believes that everyone should have some data science fluency in today's world. A born teacher, his passion for spreading data science knowledge brought him to Pluralsight as an author.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.