Multi-Class Classification

Multi-class classification with k-NN is as easy as binary classification. We just pick the class that prevails in the neighborhood.

The KNeighborsClassifier automatically performs a multi-class classification if y has more than two features, so you do not need to change anything. The only thing that changes is the y variable fed to the .fit() method.

Now, you will perform a multi-class classification with k-NN. Consider the following dataset:


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_multiple.csv')
print(df.head())

It is the same as in the previous chapter's example, but now the target can take three values:

0: "Hated it" (rating is less than 3/5);
1: "Meh" (rating between 3/5 and 4/5);
2: "Liked it" (rating is 4/5 or higher).

Task

Swipe to start coding

You are given the Star Wars ratings dataset stored as a DataFrame in the df variable.

Initialize an appropriate scaler and store it in the scaler variable.
Calculate the scaling parameters on the training data, scale it, and store the result in the X_train variable.
Scale the test data and store the result in the X_test variable.
Create an instance of k-NN with 13 neighbors, train it on the training set, and store it in the knn variable.
Make predictions on the test set and store them in the y_pred variable.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 5

Multi-Class Classification

Multi-class classification with k-NN is as easy as binary classification. We just pick the class that prevails in the neighborhood.

Now, you will perform a multi-class classification with k-NN. Consider the following dataset:


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_multiple.csv')
print(df.head())

It is the same as in the previous chapter's example, but now the target can take three values:

0: "Hated it" (rating is less than 3/5);
1: "Meh" (rating between 3/5 and 4/5);
2: "Liked it" (rating is 4/5 or higher).

Task

Swipe to start coding

You are given the Star Wars ratings dataset stored as a DataFrame in the df variable.

Initialize an appropriate scaler and store it in the scaler variable.
Calculate the scaling parameters on the training data, scale it, and store the result in the X_train variable.
Scale the test data and store the result in the X_test variable.
Create an instance of k-NN with 13 neighbors, train it on the training set, and store it in the knn variable.
Make predictions on the test set and store them in the y_pred variable.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 5

Switch to desktop for real-world practiceContinue from where you are using one of the options below