This image is almost exactly what Machine Meaning folks (such as myself) use when they talk about Bias vs. Variance (look it up, the first result will be an image such as yours). Thanks for sharing this different point of view! I've never seen these terms used this way, in Machine Learning accuracy and precision also have precise definitions but they're not quite the same. In short, accuracy is how many of your answers are correct (in a classification setting), precision is how many retrieved documents/links are relevant (in a retrieval/search perspective, often compared to recall: how many of the relevant documents were actually retrieved).
I thought this might be relevant, I'm sure more people will think of this perspective when they see your image! More evidence of the power of simple but effective images, I suppose! (I actually found your project because it was used as an example of effective analytic storytelling during my PhD)
Cheers from the Netherlands
(and yes, last week I exactly came home from some "uitwaaien" when I saw your sketch about it... Right on!)
That's super. Thanks for sharing! I learn so much doing this. I can see that the concepts of bias and variance are extremely similar.
It makes sense that in different areas specific meanings are used. I should probably amend this to refer to production and manufacturing more specifically given the alternative areas. Thanks for the ML examples.
I'm looking forward to some uitwaaien this weekend =)
This image is almost exactly what Machine Meaning folks (such as myself) use when they talk about Bias vs. Variance (look it up, the first result will be an image such as yours). Thanks for sharing this different point of view! I've never seen these terms used this way, in Machine Learning accuracy and precision also have precise definitions but they're not quite the same. In short, accuracy is how many of your answers are correct (in a classification setting), precision is how many retrieved documents/links are relevant (in a retrieval/search perspective, often compared to recall: how many of the relevant documents were actually retrieved).
I thought this might be relevant, I'm sure more people will think of this perspective when they see your image! More evidence of the power of simple but effective images, I suppose! (I actually found your project because it was used as an example of effective analytic storytelling during my PhD)
Cheers from the Netherlands
(and yes, last week I exactly came home from some "uitwaaien" when I saw your sketch about it... Right on!)
That's super. Thanks for sharing! I learn so much doing this. I can see that the concepts of bias and variance are extremely similar.
It makes sense that in different areas specific meanings are used. I should probably amend this to refer to production and manufacturing more specifically given the alternative areas. Thanks for the ML examples.
I'm looking forward to some uitwaaien this weekend =)