However, for more flexible models, there will tend to be greater variance to the model fit each time we take a set of samples to create a new training data set. In general, as we increase the number of tunable parameters in a model, it becomes more flexible, and can better fit a training data set. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. Bias and variance as function of model complexity In the lowermost image the approximated values for x=0 varies wildly depending on where the data points were located. However, depending on the noise in different trials the variance between trials increases. As spread decreases (image 3 and 4) the bias decreases: the blue curves more closely approximate the red. For a wide spread (image 2) the bias is high: the RBFs cannot fully approximate the function (especially the central dip), but the variance between different trials is low. For each trial, a few noisy data points are provided as a training set (top). A function (red) is approximated using radial basis functions (blue).
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