I wrote a little script for displaying the confusion matrix (as computed by scikit-learn), using matplotlib. The results look like this:

def show_confusion_matrix ( C , class_labels = [ '0' , '1' ]):
"""
C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function
class_labels: list of strings, default simply labels 0 and 1.
Draws confusion matrix with associated metrics.
"""
import matplotlib.pyplot as plt
import numpy as np
assert C . shape == ( 2 , 2 ), "Confusion matrix should be from binary classification only."
# true negative, false positive, etc...
tn = C [ 0 , 0 ]; fp = C [ 0 , 1 ]; fn = C [ 1 , 0 ]; tp = C [ 1 , 1 ];
NP = fn + tp # Num positive examples
NN = tn + fp # Num negative examples
N = NP + NN
fig = plt . figure ( figsize = ( 8 , 8 ))
ax = fig . add_subplot ( 111 )
ax . imshow ( C , interpolation = 'nearest' , cmap = plt . cm . gray )
# Draw the grid boxes
ax . set_xlim ( - 0.5 , 2.5 )
ax . set_ylim ( 2.5 , - 0.5 )
ax . plot ([ - 0.5 , 2.5 ],[ 0.5 , 0.5 ], '-k' , lw = 2 )
ax . plot ([ - 0.5 , 2.5 ],[ 1.5 , 1.5 ], '-k' , lw = 2 )
ax . plot ([ 0.5 , 0.5 ],[ - 0.5 , 2.5 ], '-k' , lw = 2 )
ax . plot ([ 1.5 , 1.5 ],[ - 0.5 , 2.5 ], '-k' , lw = 2 )
# Set xlabels
ax . set_xlabel ( 'Predicted Label' , fontsize = 16 )
ax . set_xticks ([ 0 , 1 , 2 ])
ax . set_xticklabels ( class_labels + [ '' ])
ax . xaxis . set_label_position ( 'top' )
ax . xaxis . tick_top ()
# These coordinate might require some tinkering. Ditto for y, below.
ax . xaxis . set_label_coords ( 0.34 , 1.06 )
# Set ylabels
ax . set_ylabel ( 'True Label' , fontsize = 16 , rotation = 90 )
ax . set_yticklabels ( class_labels + [ '' ], rotation = 90 )
ax . set_yticks ([ 0 , 1 , 2 ])
ax . yaxis . set_label_coords ( - 0.09 , 0.65 )
# Fill in initial metrics: tp, tn, etc...
ax . text ( 0 , 0 ,
'True Neg: %d \n (Num Neg: %d)' % ( tn , NN ),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 0 , 1 ,
'False Neg: %d' % fn ,
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 1 , 0 ,
'False Pos: %d' % fp ,
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 1 , 1 ,
'True Pos: %d \n (Num Pos: %d)' % ( tp , NP ),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
# Fill in secondary metrics: accuracy, true pos rate, etc...
ax . text ( 2 , 0 ,
'False Pos Rate: %.2f' % ( fp / ( fp + tn + 0. )),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 2 , 1 ,
'True Pos Rate: %.2f' % ( tp / ( tp + fn + 0. )),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 2 , 2 ,
'Accuracy: %.2f' % (( tp + tn + 0. ) / N ),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 0 , 2 ,
'Neg Pre Val: %.2f' % ( 1 - fn / ( fn + tn + 0. )),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
ax . text ( 1 , 2 ,
'Pos Pred Val: %.2f' % ( tp / ( tp + fp + 0. )),
va = 'center' ,
ha = 'center' ,
bbox = dict ( fc = 'w' , boxstyle = 'round,pad=1' ))
plt . tight_layout ()
plt . show ()