Whenever we address model prediction, errors in the forecast (bias and variance) are important to consider. There’s a tradeoff between the capacity of a model to eliminate bias and variance.
Trying to gain a good understanding of such errors will not only help us create accurate estimates but also prevent overfitting and underfitting mistakes.
What are Bias and Variance?
Bias and Variance Overview
An algorithm learns a model from the training data through supervised machine learning.
Any supervised machine learning algorithm has the task of better estimating the projection function (f) for the output variable (Y) given the input data (X).
The mapping function is also the destination function. Since it is the function approximates in a supervised machine learning algorithm.
For any machine learning algorithm the prediction error can break down into three parts:
- Bias Error
- Variance Error
- Irreducible Error
Bias is the difference between our model ‘s average prediction and the proper value developers are attempting to forecast. A model with high bias refuses to listen to the training data and makes the model oversimplified. It always leads to high errors in data from training and testing.
- Low-Bias: Indicates a few conclusions about the desired mechanism form.
- High-Bias: Indicates many conclusions about the desired mechanism form.
Variance is model prediction variability for a given data point or a value that tells us to expand our data. The high-variance model pays a lot of attention to the training data and does not generalize the data that it has not seen before. These models, therefore, score very well on training data but have high error rates on test data.
- Low Variance: Indicates small improvements in target function estimation with improvements to the training dataset.
- High Variance: Indicates significant improvements in target function estimation with modifications to the training dataset
No matter what algorithm is use the irreducible error can not reduce. The error implements from the problem’s preferred framing and may trigger factors such as unspecified variables. And also, that affect the mapping of the input variables to the output variable.
Any supervised machine learning algorithm has the goal of achieving low bias and low variance. The algorithm will achieve good performance on prediction in turn.
If our model is basic and has very few parameters, then the bias will be high and the variance small. Additionally, if the model has a lot of constraints then it will have low bias and high variance.
So without overfitting and underfitting the data, we need to find the correct/fine balance.
The complexity tradeoff is because there is a tradeoff between bias and variance. An algorithm is not too complicate and at the same time, it is less complex.
There is a trade-off between any of these two considerations and the algorithms you select. And also, the way you want to configure them to find a different balance for your issue in this trade-off.
In order to construct a good model, we must find a good balance between bias and variance such that the overall error is reduced.
Hence understanding bias and variance is key to understanding models of prediction behavior.
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