In this video, we are going to see the concepts of Overfitting vs Underfitting in Machine Learning with some basic examples.

What is Overfitting?
When our machine learning model tries to include all or more data points that are included in the dataset, this is known as overfitting. As a result, the model begins to cache inaccurate values and noise from the dataset, which lowers the model's efficiency and accuracy.

Reasons for Overfitting are as follows:
1) High variance and low bias 
2) The model is too complex
3) The size of the training data 

What is Underfitting?

When our machine learning model is unable to recognise the data's underlying trend, underfitting occurs

Reasons for Underfitting:

1) High bias and low variance 
2) The size of the training dataset used is not enough.
3) The model is too simple.
4) Training data is not cleaned and also contains noise in it.

Underfitting & Overfitting in Machine Learning:
https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/