Fitting and testing the model

In the previous exercise, you split the dataset into X_train, X_test, y_train, and y_test. These datasets have been pre-loaded for you. You'll now create a support vector machine classifier model (SVC()) and fit that to the training data. You'll then calculate the accuracy on both the test and training set to detect overfitting.

Cet exercice fait partie du cours

Dimensionality Reduction in Python

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Instructions

Import SVC from sklearn.svm and accuracy_score from sklearn.metrics
Create an instance of the Support Vector Classification class (SVC()).
Fit the model to the training data.
Calculate accuracy scores on both train and test data.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Import SVC from sklearn.svm and accuracy_score from sklearn.metrics
from ____ import ____
from ____ import ____

# Create an instance of the Support Vector Classification class
svc = ____

# Fit the model to the training data
svc.fit(____, ____)

# Calculate accuracy scores on both train and test data
accuracy_train = accuracy_score(____, svc.predict(____))
accuracy_test = accuracy_score(____, svc.predict(____))

print(f"{accuracy_test:.1%} accuracy on test set vs. {accuracy_train:.1%} on training set")

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