Multinomial Logistic Regression Model of ML

Another useful form of logistic regression is multinomial logistic regression in which the target or dependent variable can have 3 or more possible unordered types i.e. the types having no quantitative significance.

Implementation in Python

Now we will implement the above concept of multinomial logistic regression in Python. For this purpose, we are using a dataset from sklearn named digit.

First, we need to import the necessary libraries as follows −

Import sklearn
from sklearn import datasets
from sklearn import linear_model
from sklearn import metrics
from sklearn.model_selection import train_test_split

Next, we need to load digit dataset −

digits = datasets.load_digits()

Now, define the feature matrix(X) and response vector(y)as follows −

X = digits.data
y = digits.target

With the help of next line of code, we can split X and y into training and testing sets −

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 1)

Now create an object of logistic regression as follows −

digreg = linear_model.LogisticRegression()

Now, we need to train the model by using the training sets as follows −

digreg.fit(X_train, y_train)

Next, make the predictions on testing set as follows −

y_pred = digreg.predict(X_test)

Next print the accuracy of the model as follows −

print("Accuracy of Logistic Regression model is:",
metrics.accuracy_score(y_test, y_pred)*100)

Output

Accuracy of Logistic Regression model is: 95.6884561891516

From the above output we can see the accuracy of our model is around 96 percent.