log_reg_simplified.py

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load and split the digits dataset
# X, y = load_digits(return_X_y=True)
digits = load_digits()

# Separate the data and target into X and y
X = digits.data      # Feature matrix (images)
y = digits.target    # Labels (0 to 9)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Standardize features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train and evaluate models
models = {
    "SVM": SVC(kernel='linear'),
    "Logi_Regre": LogisticRegression(max_iter=10000)
}

accuracies = {}

for name, model in models.items():
    model.fit(X_train, y_train)
    y_pred= model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies[name] = accuracy

# Print individual model accuracies
#print(f"SVM Accuracy: {accuracies['SVM']:.4f}")
#print(f"Logistic Regression Accuracy: {accuracies['Logi_Regre']:.4f}")
print("SVM Accuracy: %.4f" % accuracies['SVM'])
print("Logistic Regression Accuracy: %.4f" % accuracies['Logi_Regre'])