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Classification with Python

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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())
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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).
Opgave

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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.

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book
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())
copy

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).
Opgave

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.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

close

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Completion rate improved to 4.17

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