Model Evaluation and Selection Using scikit-learn

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.
Course info
Level
Intermediate
Updated
Nov 22, 2019
Duration
1h 17m
Table of contents
Description
Course info
Level
Intermediate
Updated
Nov 22, 2019
Duration
1h 17m
Description

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.

About the author
Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Chetan Prabhu, and welcome to my course, Model Evaluation and Selection Using scikit-learn. I am a data science practitioner and leader with over 10 years of data science experience in industry. In this course, we're going to learn about techniques and methods that you can use to evaluate how well your machine learning model is performing and also how to select the best model from a set of models that you've built. Some of the major topics that we'll cover include why model evaluation and selection is important and useful, metrics and formulas used to measure the performance of classification and regression models separately, methodologies to perform model selection, avoid overfitting, and picking the model that will perform best in the future. Finally, you will learn how you can implement all of these ideas in scikit-learn, which is a major machine learning library in Python. By the end of this course, you'll be able to incorporate modern evaluation and selection techniques within your machine learning workflow all powered through scikit-learn. But before beginning this course, you should familiarize yourself with how classification and regression machine learning models are built. I will assume you are comfortable with what a machine learning model is and how they are created. I hope you'll join me on this journey to learn more about improving your machine learning workflow with the Model Selection and Evaluation in scikit-learn course, at Pluralsight.