Bias, Variance, and the Art of Building Better ML Models🎯

Machine Learning is about training models on a dataset, letting them learn patterns, and then testing them on unseen data.

Two important parameters to consider are:

🔹 Bias & Variance

High Bias → The model is too simple. It misses important patterns and performs poorly on both training and testing data.

High Variance → The model is too complex. It performs very well on training data but fails to generalize to unseen testing data.

⚡ Overfitting → High variance + low bias
Ways to reduce: choose only relevant features, reduce model complexity, or use techniques like regularization.

⚡ Underfitting → High bias + low variance
Ways to reduce: increase model complexity and use more training data.

🤔 Which is the best model?
The ideal model is one with low bias (captures patterns well) and low variance (generalizes well to new data).