非常适合机器学习

Question

I am reading an article online. It described the Overfitting and Underfitting of ML Model. I am writing the stanza of the article which I did not understand the meaning of it.please help me out to understand that what the mean of " Perform ML Algo overtime "

To understand this goal, we can look at the performance of a machine learning algorithm over time as it is learning training data. We can plot both the skill on the training data and the skill on a test dataset we have held back from the training process. Over time, as the algorithm learns, the error for the model on the training data goes down and so does the error on the test dataset. If we train for too long, the performance on the training dataset may continue to decrease because the model is overfitting and learning the irrelevant detail and noise in the training dataset. At the same time, the error for the test set starts to rise again as the model' ability to generalize decreases

Answer 1

This is talking about the bias and variance tradeoff.

We can plot both the skill on the training data

The author shouldn't use "skill", instead, using performance, which is the error from the loss function across different complexity or just Train error vs Test error across epoches.

See here: http://www.frank-dieterle.de/phd/2_8_1.html and https://scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html