Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Multi-Class Classification | k-NN Classifier
Classification with Python
course content

Course Content

Classification with Python

Classification with Python

1. k-NN Classifier
What is ClassificationWhat is k-NNk-NN with Multiple FeaturesImplementing k-NNMulti-Class ClassificationTrain-test Split. Cross ValidationChallenge: Choosing the Best K Value.k-NN Summary
2. Logistic Regression
What is Logistic RegressionFinding the ParametersChallenge: Implementing Logistic RegressionDecision BoundaryOverfitting. RegularizationChallenge: Classifying Unseparateble DataLogistic Regression Summary
3. Decision Tree
What is Decision TreeSplitting the NodesCrazy TreeChallenge: Implementing a Decision TreeDecision Tree Summary
4. Random Forest
What is Random ForestThe Randomness of ForestChallenge: Implementing a Random ForestRandom Forest Summary
5. Comparing Models
Confusion MatrixMetricsChallenge: Comparing ModelsSummary

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
test

Swipe to show code editor

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)

Once you've completed this task, click the button below the code to check your 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!

Section 1. Chapter 5
toggle bottom row

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
test

Swipe to show code editor

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)

Once you've completed this task, click the button below the code to check your 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!

Section 1. Chapter 5
toggle bottom row

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
test

Swipe to show code editor

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)

Once you've completed this task, click the button below the code to check your 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!

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
test

Swipe to show code editor

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)

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt