Bias and Variance Tradeoff

In the process of project building, bias and variance are two fundamental concepts that help us understand the behaviour and performance of predictive models.

Bias

Bias is the inability of a machine learning model to fit the training data, which means how much the predictions of a model deviate from the true values we are trying to predict.

A high-bias model tends to underfit the data, meaning it fails to capture the underlying patterns and relationships present in the data. …

bias building concepts data fundamental machine machine learning machine learning model performance predictions predictive predictive models process project training training data true values variance