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  2. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    Underfitting occurs when a mathematical model cannot adequately capture the underlying structure of the data. An under-fitted model is a model where some parameters or terms that would appear in a correctly specified model are missing. [2] Underfitting would occur, for example, when fitting a linear model to nonlinear data.

  3. Elbow method (clustering) - Wikipedia

    en.wikipedia.org/wiki/Elbow_method_(clustering)

    Example of the typical "elbow" pattern used for choosing the number of clusters even emerging on uniform data. Even on uniform random data (with no meaningful clusters) the curve follows approximately the ratio 1/k where k is the number of clusters parameter, causing users to see an "elbow" to mistakenly choose some "optimal" number of clusters.

  4. Occam's razor - Wikipedia

    en.wikipedia.org/wiki/Occam's_razor

    The bias–variance tradeoff is a framework that incorporates the Occam's razor principle in its balance between overfitting (associated with lower bias but higher variance) and underfitting (associated with lower variance but higher bias).

  5. Learning curve (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Learning_curve_(machine...

    and diagnosing problems such as overfitting (or underfitting). Learning curves can also be tools for determining how much a model benefits from adding more training ...

  6. Multidimensional scaling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_scaling

    Interpretability of the MDS solution is often important, and lower dimensional solutions will typically be easier to interpret and visualize. However, dimension selection is also an issue of balancing underfitting and overfitting. Lower dimensional solutions may underfit by leaving out important dimensions of the dissimilarity data.

  7. 7 of the most famous American investors - AOL

    www.aol.com/finance/7-most-famous-american...

    For example, he might suggest that you’re more likely to be happy by setting your expectations low or that you’ll sabotage yourself if you are envious of others and pity yourself.

  8. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    This includes, for example, early stopping, using a robust loss function, and discarding outliers. Implicit regularization is essentially ubiquitous in modern machine learning approaches, including stochastic gradient descent for training deep neural networks, and ensemble methods (such as random forests and gradient boosted trees).

  9. My daughter repeated kindergarten because she couldn't read ...

    www.aol.com/daughter-repeated-kindergarten...

    For example, my daughter wrote in her homework, "I went to the osen," rather than "I went to the ocean." The teacher hadn't corrected the mistake because the emphasis was on visual cues — a ...