(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues)
See full source and example:
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
See full source and example:
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
See full source and example : http : // scikit - learn . org / stable / auto_examples / model_selection / plot_confusion_matrix . html
def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    See full source and example:
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()
def plot_confusion_matrix ( cm , classes , normalize = False , title = 'Confusion matrix' , cmap = plt . cm . Blues ) : plt . imshow ( cm , interpolation = 'nearest' , cmap = cmap ) plt . title ( title ) plt . colorbar ( ) tick_marks = np . arange ( len ( classes ) ) plt . xticks ( tick_marks , classes , rotation = 45 ) plt . yticks ( tick_marks , classes ) if normalize : cm = cm . astype ( 'float' ) / cm . sum ( axis = 1 ) [ : , np . newaxis ] print ( "Normalized confusion matrix" ) else : print ( 'Confusion matrix, without normalization' ) thresh = cm . max ( ) / 2. for i , j in itertools . product ( range ( cm . shape [ 0 ] ) , range ( cm . shape [ 1 ] ) ) : plt . text ( j , i , cm [ i , j ] , horizontalalignment = "center" , color = "white" if cm [ i , j ] > thresh else "black" ) plt . tight_layout ( ) plt . ylabel ( 'True label' ) plt . xlabel ( 'Predicted label' ) plt . show ( )