Multi-Class Classification - Logistic Regression

"""
This simple code shows how to use Logistic regression for multi-class classification  
using the iris dataset (source: https://archive.ics.uci.edu/ml/datasets/iris).
The dataset contains 150 samples for 3 different types of irises (Setosa, Versicolour  
and Virginica).  
The rows are the samples and the columns are: Sepal Length, Sepal Width, Petal Length  
and Petal Width.
"""
# import libraries
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics

# load the iris dataset 
iris = datasets.load_iris()

# dimension of independent variables
print(iris.data.shape)

# dimension of target
print(iris.target.shape)
print()

# split the dataset into training and test
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.33, random_state=23)

# fit a logistic regression model to the data
model = LogisticRegression(solver='liblinear', multi_class='ovr')
model.fit(X_train, y_train)

# predict
actual = y_test
predicted = model.predict(X_test)

# model evaluation
# accuracy score
print("Accuracy score:", metrics.accuracy_score(actual, predicted))
print()

# classification report
print("Classification Report:")
print(metrics.classification_report(actual, predicted))
print()

# confusion matrix
print("Confusion Matrix:")
print(metrics.confusion_matrix(actual, predicted))