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:
1234import 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())
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!
Swipe to start coding
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
).
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Multi-Class Classification
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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:
1234import 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())
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!
Swipe to start coding
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
).
Løsning
Tak for dine kommentarer!
Awesome!
Completion rate improved to 3.57single