Bias-variance tradeoff
Imagine you are trying to throw a ball into a target.
If you use a very rigid and stiff arm, you might always throw the ball in the same direction, but it might not be the right direction to hit the target. This is like having high bias but low variance.
On the other hand, if you use a very loose and flexible arm, the ball might go in all sorts of directions, even if some of them hit the target. This is like having low bias but high variance.
Now, in machine learning, the same thing happens when trying to make a model that predicts outcomes. If the model is very simple and has a lot of assumptions, it might always predict similar results but miss the right answer completely. This is called having high bias.
On the other hand, if the model is very complex and tries to take into account a lot of different factors, it might predict very different outcomes for the same input, including some that are correct but many that are wrong. This is called having high variance.
Ideally, we want a model that balances both bias and variance, meaning it is not too simple but also not too complex, and is able to generalize well to new data. This is called the bias-variance tradeoff.
To achieve this, we might use techniques such as regularization, cross-validation or ensemble learning, which help us find the sweet spot between bias and variance and make accurate predictions.
If you use a very rigid and stiff arm, you might always throw the ball in the same direction, but it might not be the right direction to hit the target. This is like having high bias but low variance.
On the other hand, if you use a very loose and flexible arm, the ball might go in all sorts of directions, even if some of them hit the target. This is like having low bias but high variance.
Now, in machine learning, the same thing happens when trying to make a model that predicts outcomes. If the model is very simple and has a lot of assumptions, it might always predict similar results but miss the right answer completely. This is called having high bias.
On the other hand, if the model is very complex and tries to take into account a lot of different factors, it might predict very different outcomes for the same input, including some that are correct but many that are wrong. This is called having high variance.
Ideally, we want a model that balances both bias and variance, meaning it is not too simple but also not too complex, and is able to generalize well to new data. This is called the bias-variance tradeoff.
To achieve this, we might use techniques such as regularization, cross-validation or ensemble learning, which help us find the sweet spot between bias and variance and make accurate predictions.
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