<ul><li>Bias is the difference between the model’s predicted and actual values, indicating systematic errors due to simplistic assumptions.</li><li>High bias leads to underfitting, where the model does not capture the complexity of the data well and performs poorly on training and testing sets.</li><li>Low bias signifies the model accurately fits the training data by capturing underlying patterns and relationships.</li><li>Illustration: Trying to fit curve-shaped data points with a straight line leads to a simplistic model that misses most of the data points, showcasing the impact of bias-variance dilemma.</li></ul>

The Bias—Variance Dilemma: Why Imperfection is the Key to Better Models

Discover more