How deeply does Ensemble Methods for Machine Learning cover overfitting?

Defines the train-test failure, contrasts underfitting, and connects it to the bias-variance tradeoff without formal proofs.

Overfitting

As explained in Ensemble Methods for Machine Learning

Overfitting is a modeling failure in which a model fits training data too closely and does not necessarily generalize well to new data.

Overfitting occurs when a model fits training data too closely but generalizes poorly to new data. Training performance alone can therefore be misleading: the strongest fit on known examples may produce weaker predictions after deployment. section 1.3 section 1.3.2

Underfitting and overfitting mark opposite failures. An overly simple model performs poorly on both training and future data; an overly complex model performs very well on training data but poorly on test data. The useful target balances fit and complexity. section 1.3.2

Diagnose and control overfitting by comparing held-out performance and limiting how quickly model complexity grows:

Compare training and validation or test errors rather than selecting the model with the lowest training error. section 1.3.2 section 5.4.1
Use cross-validation to select parameters such as SVM C, learning rate, depth, or iteration count. section 1.3.2 section 8.2.3
Use bagging to aggregate unstable complex models whose errors occur in different places. section 2.2.3
Use learning-rate selection or early stopping as proactive and reactive controls during boosting. section 8.2.3 section 6.4

Increasing SVM complexity

For an RBF-kernel support vector regressor, increasing C produces more nonlinear, complex models. Training fit improves, but sufficiently large C values can reduce test-set generalization and increase overfitting.

How Ensemble Methods for Machine Learning covers it

Cover of Ensemble Methods for Machine Learning

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Related concepts

regularizationaddressed by · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
dropoutaddressed by · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.
early stoppingaddressed by · Early stopping is a technique that terminates an iterative process before its planned completion when a specified condition indicates that further work is unnecessary or unlikely to improve the objective.
underfittingcontrasts with · A modeling failure in which insufficient capacity or training prevents a model from capturing relevant patterns in the data, resulting in poor performance even on training data.
failure modekind of · A failure mode is a distinct way in which a system, component, process, or operation can fail or produce an undesirable result.
tree pruningaddressed by · Tree pruning is the removal of decision-tree branches that fail a specified split-quality condition.

This explainer was generated by Manning's knowledge graph from the text of Ensemble Methods for Machine Learning. The section links point to the parts of the book it draws on.