Precision, Recall and F1-Score

 

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score

# Assuming 'Outcome' is the target variable in your dataset

X_train, X_test, y_train, y_test = train_test_split(diabetes.loc[:, diabetes.columns != 'Outcome'], diabetes['Outcome'], stratify=diabetes['Outcome'], random_state=66)

# Create and train the KNN classifier

knn = KNeighborsClassifier(n_neighbors=9)

knn.fit(X_train, y_train)

# Print accuracy on training and test sets

print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))

print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))

# Generate and print confusion matrix

y_pred = knn.predict(X_test)

conf_matrix = confusion_matrix(y_test, y_pred)

print('\nConfusion Matrix:\n', conf_matrix)

# Calculate precision, recall, and f1-score

precision = precision_score(y_test, y_pred)

recall = recall_score(y_test, y_pred)

f1 = f1_score(y_test, y_pred)

print('\nPrecision: {:.2f}'.format(precision))

print('Recall: {:.2f}'.format(recall))

print('F1 Score: {:.2f}'.format(f1))

# Create a graphical representation of the confusion matrix using seaborn

plt.figure(figsize=(8, 6))

sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['No Diabetes', 'Diabetes'], yticklabels=['No Diabetes', 'Diabetes'])

plt.xlabel('Predicted')

plt.ylabel('Actual')

plt.title('Confusion Matrix')

plt.show()