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  2. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    Underfitting is the inverse of overfitting, meaning that the statistical model or machine learning algorithm is too simplistic to accurately capture the patterns in the data. A sign of underfitting is that there is a high bias and low variance detected in the current model or algorithm used (the inverse of overfitting: low bias and high variance).

  3. Decision tree pruning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_pruning

    Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.

  4. Double descent - Wikipedia

    en.wikipedia.org/wiki/Double_descent

    An example of the double descent phenomenon in a two-layer neural network: ... as it contradicts assumptions about overfitting in classical machine learning. [1]

  5. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Statistical learning theory - Wikipedia

    en.wikipedia.org/wiki/Statistical_learning_theory

    This image represents an example of overfitting in machine learning. The red dots represent training set data. The green line represents the true functional relationship, while the blue line shows the learned function, which has been overfitted to the training set data. In machine learning problems, a major problem that arises is that of ...

  8. One in ten rule - Wikipedia

    en.wikipedia.org/wiki/One_in_ten_rule

    In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk of overfitting and finding spurious correlations low. The rule states that one ...

  9. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    Keeping a function simple to avoid overfitting may introduce a bias in the resulting predictions, while allowing it to be more complex leads to overfitting and a higher variance in the predictions. It is impossible to minimize both simultaneously.