Bias, variance, Overfit, underfit

Little Story:

Venkat, the human textbook, memorized every word but found himself stuck in a mundane job. Pari, who grasped concepts instead of parroting words, soared to success as a data scientist. Senthil, who barely engaged with learning, ended up jobless.

From this awesome story, we can understand that

Venkat- performs well in Training but fails in testing - overfit (low bias, high variance)

Senthil- performs worst in training and testing - underfit (high bias, high variance)

Pari - performs well in training and testing - low bias and low variance

Note: It's important to note that even Pari, despite his success, may still have some variance and bias.

Overfitting:

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and underlying patterns. It's like memorizing answers without understanding the concepts. The model fits the training data perfectly but fails to generalize to unseen data, resulting in poor performance.

Underfitting:

Underfitting happens when a model is too simplistic to capture the underlying structure of the data. It's akin to oversimplifying a complex problem, resulting in inadequate predictions even on the training data.

Bias: Error in Training

Bias refers to the error introduced by approximating a real-world problem with a simplified model. High-bias models, like linear regression with few features, may oversimplify the data and consistently miss the mark. Low-bias models, such as complex neural networks, capture intricate relationships more accurately.

Variance: Error in Testing

Variance measures the model's sensitivity to small fluctuations in the training data. Models with high variance, like decision trees with no constraints, tend to overreact to noise in the training set. Models with low variance, like linear regression, produce more stable predictions across different datasets.

Bias-Variance Tradeoff:

The bias-variance tradeoff is the balance between the simplicity and complexity of a model. Like Pari, Pari did a good amount of bias and variance so he got a good job, which means he has a good bias variance tradeoff!

The total error in a model can be represented as:

Striking a proper balance between bias and variance is key to developing a model that generalizes effectively to new data. Models with high bias are simpler and may miss significant patterns, whereas models with high variance are complex and can overfit. The objective is to minimize both bias and variance to achieve optimal predictive performance. Techniques such as regularization, cross-validation, and model selection are instrumental in managing this balance.

Comprehending these concepts and implementing suitable strategies are vital in creating robust and precise machine-learning models. Achieving the correct equilibrium between bias and variance enables the development of models that excel in training data and generalize well to novel data.

Resources:

The Bias-Variance Trade-Off : A Mathematical View | by Mansi Goel | SNU.ai | Medium

Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting (youtube.com)

Let Your Ideas Shine Through | Better Grades With Grammarly (youtube.com)