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Bias Vs Variance Trade off | Machine Learning | Tamil

The bias-variance tradeoff is a fundamental concept in machine learning that involves finding the right balance between simplicity and complexity when building a model. Imagine you're trying to hit a target with a bow and arrow. Bias refers to the error caused by overly simplistic assumptions. If you consistently miss the target in the same direction (e.g., always shooting to the left), your model has high bias. This can happen when the model is too simple and doesn't capture the underlying patterns in the data. Variance, on the other hand, refers to the error caused by excessive complexity. If you shoot arrows that land all over the place with no consistent pattern, your model has high variance. This occurs when the model is too complex and captures noise or random fluctuations in the data. Finding the right balance between bias and variance is crucial. A model with high bias and low variance may consistently miss the target, while a model with low bias and high variance may be inconsistent and unpredictable. To achieve the best performance, you want to minimize both bias and variance simultaneously, which is often challenging. It's like trying to hit the bullseye consistently without being too rigid or too erratic. To address bias, you can use more complex models, increase the number of features, or enhance the model's flexibility. To tackle variance, you can use regularization techniques, gather more training data, or reduce the model's complexity. Understanding the bias-variance tradeoff helps you strike a balance between simplicity and complexity, allowing you to create models that generalize well to unseen data and achieve optimal accuracy in machine learning tasks.