tuto_2.py

# Predict categories using Logistic Regression module

from sklearn import datasets, metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris()

print(iris.keys())
print(iris.target_names)
print(iris.feature_names)
print(iris.data[:3])
print(iris.target[:3])
print(iris.data.shape)
print(iris.target.shape)

X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)
model = LogisticRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

print(y_test)
print(predictions)
print(model.score(X_test, y_test))
print(metrics.accuracy_score(y_test, predictions))
print(metrics.classification_report(y_test, predictions))
print(metrics.confusion_matrix(y_test, predictions))