What are the three types of error in a #ML model?

👉 1. Bias - error caused by choosing an algorithm that cannot accurately model the signal in the data, i.e. the model is too general or was incorrectly selected. For example, selecting a simple linear regression to model highly non-linear data would result in error due to bias.

👉 2. Variance - error from an estimator being too specific and learning relationships that are specific to the training set but do not generalize to new samples well. Variance can come from fitting too closely to noise in the data, and models with high variance are extremely sensitive to changing inputs. Example: Creating a decision tree that splits the training set until every leaf node only contains 1 sample.

👉 3. Irreducible error - error caused by noise in the data that cannot be removed through modeling. Example: inaccuracy in data collection causes irreducible error.

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