underfitting linear regression

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_breast_cancer

# Load the Breast Cancer Dataset
data = load_breast_cancer()
X = data.data
y = data.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a simple linear regression model
lr = LinearRegression()
lr.fit(X_train, y_train)

# Calculate training and testing errors
train_error = mean_squared_error(y_train, lr.predict(X_train))
test_error = mean_squared_error(y_test, lr.predict(X_test))

print(f'Training error: {train_error:.2f}')
print(f'Testing error: {test_error:.2f}')