Conteúdo do Curso
Classification with Python
5. Comparing Models
Classification with Python
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:
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!
Tarefa
Perform a classification using the KNeighborsClassifier
with n_neighbors
equal to 13
.
- Import the
KNeighborsClassifier
. - Use the appropriate class to scale the data.
- Scale the data using
.fit_transform()
for training data and.transform()
for new instances. - Create the
KNeighborsClassifier
object and feedX_scaled
andy
to it. - 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.
Tudo estava claro?
Conteúdo do Curso
Classification with Python
5. Comparing Models
Classification with Python
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:
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!
Tarefa
Perform a classification using the KNeighborsClassifier
with n_neighbors
equal to 13
.
- Import the
KNeighborsClassifier
. - Use the appropriate class to scale the data.
- Scale the data using
.fit_transform()
for training data and.transform()
for new instances. - Create the
KNeighborsClassifier
object and feedX_scaled
andy
to it. - 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.
Tudo estava claro?