What is a Learning Curve in machine learning?




What is a Learning Curve in machine learning?

The learning curve is just the notion that certain things require more time to learn. This can be due to the amount of rules it has to learn or the level of competency you have to have to be able to do it. Like for playing a video game, you have to know how to play it, then learn the tips and tricks, and then learn what the pro players do, and then you can play it very well.

But there is that initial climb that you have to get over first. To best get over it, is just the practice and have the mindset that you can do it. If you do not have this mindset then you will constantly think about quitting. As someone who has learned a lot of skills and it currently learning more, it is just practice at the end of the day.

The more time you put in, the better you get. Most people don’t understand this and you get people who study a language for years and can’t speak a word because they didn’t practice how to speak it. If you are learning a motor skill, if you do another task that involves that same motor skill, it helps you learn it twice as fast. So there are ways around motor skills, but other skills you have to just practice.

A learning curve is the generalized two-dimensional graph depicting how knowledge is acquired in varying degrees at the times specified. Each student’s learning curve is unique to them (and may also vary considerably with varied topics/sources). It basically shows how learning occurs over a time-dependent arc.

The graph plots comprehension vs. time. The longer the time devoted to study, the greater the degree of comprehension (in theory, anyhow). For most people it is a curve, but it could also be in the form of a rather straight-sloped linear graph (with a taller slope indicating a faster learner). Better teaching methods can change learning curves, however slope/speed of knowledge acquisition should never be the sole aim of education.

Useful for generalizing (when carefully matched or tailored to individual students), but not for setting policies applicable to all students (because humans differ and need individualized instruction/resource allocation).

How it works: Basically at first, little is learned over some period, then the rate of learning accelerates, and then after comprehension reaches its peak for the learner(s) or material, it usually decelerates about as rapidly as it accelerated, then it more gradually diminishes in a way that simulates the reverse of the initial phase.

See also Bell Curve or Standard Distribution Curve. Note that rigid conformance to a belief that an arbitrary curve is applicable to all students is patently unfair because not all students learn at the same rate given identical conditions.

The expression “bell curve” has also been used to unfairly discriminate against students by pretending to depict some universal reality, when in fact it is actually only an abstract construct intended to grossly explain phenomenae which are far more subtle and complex.

Use of this type of depiction to shape coursework grading has been proven to be generally-harmful pseudoscience. This expression was very controversial 50–70 years ago among pedagogues and social scientists for this reason as arbitrary grading “cutoff values” were set and applied to all students as if they were all capable of learning at the same rate.

The most practical implication for one’s learning curve is to have a solid grasp on how to address it in order to master various subjects: those which tend to have especially-steep learning curves may benefit from varied instruction approaches (like private tutoring).

Those subjects with shallower curves may be suitable for learning multiple topics simultaneously or in larger-sized classes because students will usually find the material less challenging to comprehend/learn.