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.
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 −
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)
Accuracy of Logistic Regression model is: 95.6884561891516
From the above output we can see the accuracy of our model is around 96 percent.