Fitting multi-class logistic regression

In this exercise, you'll fit the two types of multi-class logistic regression, one-vs-rest and softmax/multinomial, on the handwritten digits data set and compare the results. The handwritten digits dataset is already loaded and split into X_train, y_train, X_test, and y_test.

Deze oefening maakt deel uit van de cursus

Linear Classifiers in Python

Cursus bekijken

Oefeninstructies

Fit a one-vs-rest logistic regression classifier by setting the multi_class parameter and report the results.
Fit a multinomial logistic regression classifier by setting the multi_class parameter and report the results.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Fit one-vs-rest logistic regression classifier
lr_ovr = ____
lr_ovr.fit(X_train, y_train)

print("OVR training accuracy:", lr_ovr.score(X_train, y_train))
print("OVR test accuracy    :", lr_ovr.score(X_test, y_test))

# Fit softmax classifier
lr_mn = ____
lr_mn.fit(X_train, y_train)

print("Softmax training accuracy:", lr_mn.score(X_train, y_train))
print("Softmax test accuracy    :", lr_mn.score(X_test, y_test))

Code bewerken en uitvoeren