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- Bias–variance_dilemma abstract "In machine learning, the bias–variance dilemma or bias–variance tradeoff is the problem of simultaneously minimizing the bias (how accurate a model is across different training sets) and variance of the model error (how sensitive the model is to small changes in training set). This tradeoff applies to all forms of supervised learning: classification, function fitting, and structured output learning.The bias–variance tradeoff is a central problem in supervised learning. Ideally, one wants to choose a model that at the same time captures the regularities in its training data, but also generalizes well to unseen data. Models with high bias are intuitively simple models: they impose restrictions on the kind of regularities that can be learned (examples include linear classifiers). The problem with these models is that they underfit, i.e. not learn the relationship between predicted (target) variables and features. Models with high variance are those that can learn many kinds of complex regularities—but that includes the possibility to learn noise in the training data, i.e. overfitting. To achieve good performance on data outside the training set, a tradeoff must be made.".
- Bias–variance_dilemma thumbnail Test_function_and_noisy_data.png?width=300.
- Bias–variance_dilemma wikiPageID "40678189".
- Bias–variance_dilemma wikiPageRevisionID "605659422".
- Bias–variance_dilemma align "right".
- Bias–variance_dilemma caption "Function and noisy data.".
- Bias–variance_dilemma caption "spread=0.1".
- Bias–variance_dilemma caption "spread=1".
- Bias–variance_dilemma caption "spread=5".
- Bias–variance_dilemma direction "vertical".
- Bias–variance_dilemma footer "A function is approximated using radial basis functions . Several trials are shown in each graph. For each trial, a few noisy data points are provided as training set . For a wide spread the bias is high: the RBFs cannot fully approximate the function , but the variance between different trials is low. As spread decreases the bias decreases: the blue curves more closely approximate the red. However, depending on the noise in different trials the variance between trials increases. In the lowermost image the approximated values for x=0 varies wildly depending on where the data points were located.".
- Bias–variance_dilemma image "Radial basis function fit, spread=0.1.png".
- Bias–variance_dilemma image "Radial basis function fit, spread=1.png".
- Bias–variance_dilemma image "Radial basis function fit, spread=5.png".
- Bias–variance_dilemma image "Test function and noisy data.png".
- Bias–variance_dilemma width "200".
- Bias–variance_dilemma subject Category:Dilemmas.
- Bias–variance_dilemma subject Category:Machine_learning.
- Bias–variance_dilemma subject Category:Model_selection.
- Bias–variance_dilemma subject Category:Statistical_classification.
- Bias–variance_dilemma comment "In machine learning, the bias–variance dilemma or bias–variance tradeoff is the problem of simultaneously minimizing the bias (how accurate a model is across different training sets) and variance of the model error (how sensitive the model is to small changes in training set). This tradeoff applies to all forms of supervised learning: classification, function fitting, and structured output learning.The bias–variance tradeoff is a central problem in supervised learning.".
- Bias–variance_dilemma label "Bias–variance dilemma".
- Bias–variance_dilemma sameAs Bias%E2%80%93variance_dilemma.
- Bias–variance_dilemma sameAs Q17003119.
- Bias–variance_dilemma sameAs Q17003119.
- Bias–variance_dilemma wasDerivedFrom Bias–variance_dilemma?oldid=605659422.
- Bias–variance_dilemma depiction Test_function_and_noisy_data.png.
