"Machine learning models represent the learning output from a machine in such a way that, it can be used in the future to predict or understand similar kinds of data by which the model had been trained.
In the process of building a Machine Learning model, there is a trade-off between bias and variance. We all know this.
But what exactly does it mean when we say a model has a high bias or high variance? Can we visualize what is happening?"