Hi,

From what I have heard, bias in ML is related to underfitting and overfitting. Whereas bias in DL is a parameter. Are they different or one and the same?

Hi,

From what I have heard, bias in ML is related to underfitting and overfitting. Whereas bias in DL is a parameter. Are they different or one and the same?

Hi @albela_angur,

A good and truly an *albela* question. The intuition of **bias** in machine learning actually comes with bias vs. variance tradeoff which refers to choosing an optimal point without overfitting or underfitting a model.

The bias in the deep learning is a parameter in keras for a Dense layer. To be precise its **use_bias** and a boolean parameter. The parameter is `boolean`

type which allows a to use a bias vector. Its mostly used to shift the activation function and the decision split. A simple example - If activation function splits a class into 1 or 0 based on `if probability > 0.5 = 1 else 0`

.

Now, with **use_bias = True** the model will use another weight `wb`

that shifts the split to `if probability > 0.7 = 1 else 0`

after its trained.

Hope this answered your question. Have a great day.