Today, is my 98th day of #100daysofcode and #python learning journey. Today sometimes I learned more about streamlit. Like usual day today I also kept learning from DataCamp about the topic Cross validation and Confusion Matrix.
While studying I learned that confusion matrix counts the number of instance when the model predicted the outcome of an event and measure it against the actual value. Similarly cross validation maximize the availability of training data by splitting data into various combination and testing each specific combination.
# 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)
Output of the codewill be
[[176 30] [ 52 50]] precision recall f1-score support 0 0.77 0.85 0.81 206 1 0.62 0.49 0.55 102 avg / total 0.72 0.73 0.72 308