Bias-Variance Tradeoff

Add regulation will decrease the variance and add more bias

ideal model typically exhibits low bias and low variance. Let's understand these terms:

1. Bias: Bias refers to the error introduced by approximating a real-world problem with a simplified model. A model with high bias tends to oversimplify the problem and may fail to capture important patterns in the data. High bias often leads to underfitting, where the model performs poorly both on the training data and unseen data.

2. Variance: Variance refers to the model's sensitivity to fluctuations in the training data. A model with high variance fits the training data too closely, capturing noise in the data rather than the underlying patterns. High variance often leads to overfitting, where the model performs well on the training data but poorly on unseen data.

The ideal model strikes a balance between bias and variance, achieving low levels of both. Such a model generalizes well to unseen data, capturing the underlying patterns without being overly sensitive to noise. This balance results in good performance on both the training and test datasets.

Finding this balance is a key goal in machine learning model selection and training. Techniques such as cross-validation, regularization, and ensemble methods like bagging and boosting are commonly used to achieve models with low bias and low variance.