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Classification with Python
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Contenido del Curso

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!

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 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!

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 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!

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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!

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 5
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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