Bias and variance;

[Deepti_Prasad]

hi @anon88576143

That statement about high bias and high variance is based on train set error and dev (validation) set error rate

Here is a discussion which explains a little more detail using the course material as well as other data

High Bias Problem

Hello, If you have taken DLS specialisation, Professor Ng explains about splitting data ratio. You can refer Course 2 Improving Deep Neural Networks 1st weeks’ videos. Especially for a data too small, your split ratio will cause underfitting the data. [Screenshot 1945-06-20 at 9.23.30 AM] A general rule usually used or applied in algorithm is at least ten times as many rows (data points) as there are features (columns) in your dataset. This means that if your dataset has 10 columns (i.e., …

In some cases, when a model is created using a validation and train set dataset and then used in test dataset, the model might have given greater result on the train set with validation set (overfit) where as when the same model was tested on test set must have shows a poor performance indicating high bias on train set compared to test set.

Regards
DP