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机器学习:5.1 模型组合 Model Combination

机器学习:5.1 模型组合 Model Combination

作者: Cache_wood | 来源:发表于2022-04-15 15:39 被阅读0次

    @[toc]

    Bias & Variance Decomposition

    • Learn \hat{f}_D from dataset D sampled from y= f(x) +\varepsilon

    • Evaluate generalization error (y-\hat{f}_D(x))^2 on a new data point (x,y)
      E_D[(y-\hat{f}_D(x))^2] = E_D[((f-E_D[\hat{f}_D])-(\hat{f}_D -E_D[\hat{f}_D])+\varepsilon)^2]\\ = (f-E_D[\hat{f}_D])^2+E_D[(\hat{f}_D-E_D[\hat{f}_D])^2]+\varepsilon^2\\ = Bias[\hat{f}_D]^2+Var[\hat{f}_D]+\varepsilon^2

    Reduce Bias & Variance

    • Reduce bias
      • A more complex model
        • e.g. increase layers, hidden units of MLP
        • Boosting, Stacking
    • Reduce variance
      • A simpler model
        • e.g. regularization
        • Bagging, Stacking
    • Reduce \sigma^2
      • Improve data
    • Ensemble learning: train and combine multiple models to improve predictive performance

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