If we reduce size of training dataset does it decreases bias? I'm a newbie and learning ML. I've a doubt, normally we know we should increase the size of training dataset or should add more data to reduce variance (fairly understood why). Now variance has inverse relationship with bias, so it means when we're adding more data, we're reducing variance - or we're increasing bias. Then, why this is not possible to reduce bias by reducing the number of training samples. Could someone please explain me.

You can only speak of 'inverse relation' when you change model complexity (including, to some extent, feature selection vs feature addition).

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*As the number of samples grows, variance drops, bias is unchanged.

*Increasing the model complexity decreases bias but increases variance.

*Dropping irrelevant features decreases variance, adding relevant features decreases bias.

