7. Build the Model

建立一些模型，比較不同模型在不同的參數設定下對 iris dataset 的分類能力。

Model 1: Logistic Regression

第一個模型未使用 Logistic Regression 邏輯斯迴歸，在統計的領域中也稱作 Logit Model，他是一種非線性的廻歸模型，

from sklearn.linear_model import LogisticRegression
# https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py

# create classifier
clf = LogisticRegression(C=20, penalty="l2", solver="saga",\
              multi_class='multinomial', tol=0.0001, random_state=20241006)

# train classifier
clf.fit(X_train, y_train)


# predict
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)

# Evaluate the Model
from sklearn.metrics import confusion_matrix

print(confusion_matrix(y_train, y_pred_train))
print()
print(confusion_matrix(y_test, y_pred_test))

from sklearn.metrics import classification_report

print('\n Train ***********************************')
print('Accuracy: {:.2f}'.format(accuracy_score(y_train, y_pred_train)))
print('Confusion matrix = {}'.format(confusion_matrix(y_train, y_pred_train)))
print(confusion_matrix(y_train, y_pred_train))
print(classification_report(y_train, y_pred_train))
print('\n Test ************************************')
print('Accuracy: {:.2f}'.format(accuracy_score(y_test, y_pred_test)))
print(classification_report(y_test, y_pred_test)