model_development_loan_approval_system.py
def plot_confusion_matrix(cm, classes, title ='Confusion matrix', normalize = False, cmap = plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix')

    print(cm)

    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)

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

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()

    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
This function prints and plots the confusion matrix . Normalization can be applied by setting ` normalize = True ` .
def plot_confusion_matrix(cm, classes, title ='Confusion matrix', normalize = False, cmap = plt.cm.Blues):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix')
    print(cm)
    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)
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()
def plot_confusion_matrix ( cm , classes , title = 'Confusion matrix' , normalize = False , cmap = plt . cm . Blues ) : if normalize : cm = cm . astype ( 'float' ) / cm . sum ( axis = 1 ) [ : , np . newaxis ] print ( "Normalized confusion matrix" ) else : print ( 'Confusion matrix' ) print ( cm ) 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 ) fmt = '.2f' if normalize else 'd' thresh = cm . max ( ) / 2. for i , j in itertools . product ( range ( cm . shape [ 0 ] ) , range ( cm . shape [ 1 ] ) ) : plt . text ( j , i , format ( cm [ i , j ] , fmt ) , horizontalalignment = "center" , color = "white" if cm [ i , j ] > thresh else "black" ) plt . ylabel ( 'True label' ) plt . xlabel ( 'Predicted label' ) plt . tight_layout ( )
This function prints and plots the confusion matrix.
This function prints and plots the confusion matrix .
"""
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
cm
classes
title
normalize
cmap