A Practical Approach to Timeseries Forecasting Using Python - Model Evaluation for Underfitting and Overfitting

The video tutorial discusses the concept of epochs in model training, focusing on how varying the number of epochs affects model performance, particularly in terms of overfitting and underfitting. It explains the importance of finding an optimal number of epochs to achieve model stability with low bias and variance. The tutorial also previews future topics, including LSTM models and their applications.

What is the ideal stability level for a model, and why is it important?

Discuss the relationship between bias and variance in the context of model evaluation.

What are the next steps mentioned for working with LSTMs after discussing epochs?