What is a Learning Curve in machine learning?

Question

I want to know what a learning curve in machine learning is. What is the standard way of plotting it? I mean what should be the x and y axis of my plot?

Answer 1

Some people use "learning curve" to refer to the error of an iterative procedure as a function of the iteration number, i.e., it illustrates convergence of some utility function. In the example below, I plot mean-square error (MSE) of the least-mean-square (LMS) algorithm as a function of the iteration number. That illustrates how quickly LMS "learns", in this case, the channel impulse response.

Answer 2

In Andrew's machine learning class, a learning curve is the plot of the training/cross-validation error versus the sample size. The learning curve can be used to detect whether the model has the high bias or high variance. If the model suffers from high bias problem, as the sample size increases, training error will increase and the cross validation error will decrease and at last they will be very close to each other but still at a high error rate for both training and classification error. And increasing the sample size will not help much for high bias problem.

If the model suffers from high variance, as the keep increasing the sample size, the training error will keep increasing and cross-validation error will keep decreasing and they will end up at a low training and cross-validation error rate. So more samples will help to improve the model prediction performance if the model suffer from high variance.

Answer 3

It is a Graph that compares the performance of a model on preparing and testing data over a changing number of training instances and these are a generally utilized as analytic instrument in machine learning for calculations that learn from a training dataset incrementally. It allows us to verify when a model has learning as much as it can about the data.

There are three kinds of expectations to Learning curves absorb information

• Bad Learning Curve: High Bias
• Bad Learning Curve: High Variance
• Ideal Learning Curve