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Classification with Python

bookMulti-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).

Let's move to classification! Well, wait, here is the reminder of the classes you will use.

And now, let's move to classification!

Task

Swipe to start coding

Perform a classification using the KNeighborsClassifier with n_neighbors equal to 13.

  1. Import the KNeighborsClassifier.
  2. Use the appropriate class to scale the data.
  3. Scale the data using .fit_transform() for training data and .transform() for new instances.
  4. Create the KNeighborsClassifier object and feed X_scaled and y to it.
  5. Predict the classes for new instances (X_new_scaled).

Solution

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bookMulti-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).

Let's move to classification! Well, wait, here is the reminder of the classes you will use.

And now, let's move to classification!

Task

Swipe to start coding

Perform a classification using the KNeighborsClassifier with n_neighbors equal to 13.

  1. Import the KNeighborsClassifier.
  2. Use the appropriate class to scale the data.
  3. Scale the data using .fit_transform() for training data and .transform() for new instances.
  4. Create the KNeighborsClassifier object and feed X_scaled and y to it.
  5. Predict the classes for new instances (X_new_scaled).

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 5
single

single

close

Awesome!

Completion rate improved to 3.57

bookMulti-Class Classification

Swipe to show menu

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).

Let's move to classification! Well, wait, here is the reminder of the classes you will use.

And now, let's move to classification!

Task

Swipe to start coding

Perform a classification using the KNeighborsClassifier with n_neighbors equal to 13.

  1. Import the KNeighborsClassifier.
  2. Use the appropriate class to scale the data.
  3. Scale the data using .fit_transform() for training data and .transform() for new instances.
  4. Create the KNeighborsClassifier object and feed X_scaled and y to it.
  5. Predict the classes for new instances (X_new_scaled).

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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