COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
Question
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Variance


Overfitting


Underfitting


Bias

Detailed explanation1: Technically, we can define bias as the error between average model prediction and the ground truth. Moreover, it describes how well the model matches the training data set: A model with a higher bias would not match the data set closely. A low bias model will closely match the training data set.
Detailed explanation2: While making predictions, a difference occurs between prediction values made by the model and actual values/expected values, and this difference is known as bias errors or Errors due to bias.
Detailed explanation3: The bias is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs. In other words, model with high bias pays very little attention to the training data and oversimpliies the model.
Detailed explanation4: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Tradeoff is tension between the error introduced by the bias and the variance.