Trying to make compelling plot for classification results with python
It's really rare that you'd show a plot of the probabilities for each example in your set. Are you sure you want to do this? A better presentation might be a confusion matrix. Here's how it works:

1) The columns are the true class labels

2) The rows are the predicted classes

3) Along the right hand side of the plot you can show the probability of correctly assigning to a class (or the classification error, if you prefer).

For example, say I have three classes in my dataset. 
I have 10 examples of each class, so 30 examples total.

My classification results from my model are:

8 of the 10 examples in class 1 were correctly labelled; 1 was misclassified to class 2 and 1 was misclassified to class 3.

7 of the 10 examples in class 2 were correctly labelled; 3 were misclassified to class 3.

9 of the 10 examples in class 3 were correctly labelled; 1 was misclassified to class 2.

Then my confusion matrix looks like this:

$\begin{bmatrix} 0.8 &amp; 0.1 &amp; 0.1 \\ 0.0 &amp; 0.7 &amp; 0.3 \\ 0.0 &amp; 0.1 &amp; 0.9 \end{bmatrix}$

And along the right hand side of my confusion matrix I can include the classification error: 0.2 for class 1; 0.3 for class 2; 0.1 for class 3.

Notice the rows must add to 1.