Bias refers to the error introduced by approximating a real-world problem (which may be complex) with a simplified model.
High bias can cause a model to miss relevant relationships between features and target outputs, leading to underfitting.
On the other hand, variance, on the other hand, is the error caused by the model's sensitivity to small fluctuations in the training data. A model with high variance may perform well on training data but poorly on new, unseen data this is called overfitting. The bias-variance tradeoff involves finding the right balance to minimize total error. You can learn more from Bias and Variance in Machine Learning or explore scikit-learn's guide on model evaluation.
Amira BedhiafiPosted May 27, 2025, 7:04 PM
Bias refers to the error introduced by approximating a real-world problem (which may be complex) with a simplified model.
High bias can cause a model to miss relevant relationships between features and target outputs, leading to underfitting.
On the other hand, variance, on the other hand, is the error caused by the model's sensitivity to small fluctuations in the training data. A model with high variance may perform well on training data but poorly on new, unseen data this is called overfitting. The bias-variance tradeoff involves finding the right balance to minimize total error. You can learn more from Bias and Variance in Machine Learning or explore scikit-learn's guide on model evaluation.