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 <[email protected]>
> 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
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