from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Load iris dataset
iris = load_iris()

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# Train a logistic regression model on the initial training data
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

# Evaluate the model's accuracy on the initial test data
print("Accuracy on initial test data:", logreg.score(X_test, y_test))

# Increase the size of the training data
X_train_new, _, y_train_new, _ = train_test_split(iris.data, iris.target, test_size=0.7, random_state=42)

# Retrain the model on the increased training data
logreg_new = LogisticRegression()
logreg_new.fit(X_train_new, y_train_new)

# Evaluate the model's accuracy on the initial test data
print("Accuracy on increased training data:", logreg_new.score(X_test, y_test))