# Importing the dependancies
from sklearn import metrics

# Predicted values
y_pred = [0, 1, 1, 0, 1, 1, 1, 1]

# Actual values
y_act = [0, 1, 0, 0, 1, 1, 1, 1]

# Printing the confusion matrix
# The columns will show the instances predicted for each label,
# and the rows will show the actual number of instances for each label.
cm = metrics.confusion_matrix(y_act, y_pred, labels=[0, 1])

# Printing the precision and recall, among other metrics
cr = metrics.classification_report(y_act, y_pred, labels=[0, 1])