Hi, Suranga

So, I may have to go over 2 models, so McNamara's may not be an option :(

Sure, but there are many other hypothesis tests, was just a suggestion since I thought you just wanted compare 2 models :)

plt.boxplot(results) So what does "results" look like?

[0.85433808345719897, 0.8976733724549345]

You can’t do a boxplot based on 1 single value.

These are the two precision values calculated for each neural network. Exactly what should 1Darray_of_model1_results look like? is it one value per model or....

This should work: model_1 = [0.85, # experiment 1 0.84] # experiment 2 model_2 = [0.84, # experiment 1 0.83] # experiment 2 plt.boxplot([model_1, model_2]) However, a boxplot based on 2 values only doesn’t make sense imho, I you could just plot the range. Best, Sebastian

On Oct 30, 2016, at 4:43 PM, Suranga Kasthurirathne <surangakas@gmail.com> wrote:

Hi Sebastian!

Thank you, you might be onto something here ;)

So, I may have to go over 2 models, so McNamara's may not be an option :(

In regard to your second comment, in building my boxplots, this is how I input results.

plt.boxplot(results) So what does "results" look like?

[0.85433808345719897, 0.8976733724549345]

These are the two precision values calculated for each neural network. Exactly what should 1Darray_of_model1_results look like? is it one value per model or....

-- Best Regards, Suranga _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn