macierz zamieszania do klasyfikacji
# Import necessary modules
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
# Create training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)
# Instantiate a k-NN classifier: knn
knn = KNeighborsClassifier(n_neighbors=6)
# Fit the classifier to the training data
knn.fit(X_train, y_train)
# Predict the labels of the test data: y_pred
y_pred = knn.predict(X_test)
# Generate the confusion matrix and classification report
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
josh.ipynb