From the course: More Python Tips, Tricks, and Techniques for Data Science
Join today to access over 22,600 courses taught by industry experts or purchase this course individually.
Model validation and hyperparameter tuning - Python Tutorial
From the course: More Python Tips, Tricks, and Techniques for Data Science
Model validation and hyperparameter tuning
- [Instructor] For our machine learning model to make an informed decision, we need a way to validate data model and the set of hyperparameters are a good fit for the data. So this may sound very simple, but there are a few pitfalls that we must avoid. So to demonstrate this, we are going to look at the load Iris data set and a few models to validate using different methods. So, I've got the load Iris data set. So this is a classic plant data set, which is a very easy multi-class classification problem. And we've got a few features. So, let's store the features in X using the data attribute and store the labels in Y using the target attribute. And if you want to look at the shape of the data, so it contains 150 rows and four columns. If you want to learn more about the data set, you can use the describe attribute and it will give you all the information about the plant data set, the Iris data set from the sacred land…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.