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Shared February 6, 2026
Machine Learning Bias - Variance Tradeoff
Bias / Variance ( Under - Fitting VS Over - Fitting )
Underfitting occurs when a machine learning model is too simple to capture the underlying structure of data, resulting in poor performance on both training and test datasets. Characterized by high bias and low variance, it indicates the model cannot learn the,,meaningful patterns in the data. Common causes include insufficient training time, overly simple models (e.g., linear models for non-linear data), and excessive regularization.
Overfitting is a machine learning modeling error where a model learns training data too well, capturing noise and specific, non-generalizable patterns instead of the underlying structure. This results in high training accuracy but poor, inconsistent performance on new, unseen data, often caused by excessive model complexity or insufficient training data.
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