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ClassificationDiscriminant


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 -- statistics: ClassificationDiscriminant

     Discriminant analysis classification

     The ‘ClassificationDiscriminant’ class implements a linear discriminant
     analysis classifier object, which can predict responses for new data using
     the ‘predict’ method.

     Discriminant analysis classification is a statistical method used to
     classify observations into predefined groups based on their
     characteristics.  It estimates the parameters of different distributions
     for each class and predicts the class of new observations by finding the
     one with the smallest misclassification cost.

     Create a ‘ClassificationDiscriminant’ object by using the ‘fitcdiscr’
     function or the class constructor.

     See also: fitcdiscr.


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Discriminant analysis classification



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ClassificationGAM


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 -- statistics: ClassificationGAM

     Generalized additive model classification

     The ‘ClassificationGAM’ class implements a gradient boosting algorithm for
     classification, using spline fitting as the weak learner.  This approach
     allows the model to capture non-linear relationships between predictors and
     the binary response variable.

     Generalized additive model classification is a statistical method that
     extends linear models by allowing non-linear relationships between each
     predictor and the response variable through smooth functions.  It combines
     the interpretability of linear models with the flexibility of
     non-parametric methods.

     Create a ‘ClassificationGAM’ object by using the ‘fitcgam’ function or the
     class constructor.

     See also: fitcgam.


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Generalized additive model classification



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ClassificationKNN


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 -- statistics: ClassificationKNN

     K-nearest neighbors classification

     The ‘ClassificationKNN’ class implements a K-nearest neighbor classifier
     object, which can predict responses for new data using the ‘predict’
     method.  The implemented algorithm allows you choose a range of different
     distance metrics, the number of nearest neighbors, as well as the searching
     algorithm.

     The K-nearest neighbors (k-NN) classifier is a simple, non-parametric
     machine learning algorithm used for classification tasks.  It classifies a
     data point based on the majority class of its k closest neighbors in the
     feature space.

     Create a ‘ClassificationKNN’ object by using the ‘fitcknn’ function or the
     class constructor.

     See also: fitcknn.


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K-nearest neighbors classification



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ClassificationNeuralNetwork


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 -- statistics: ClassificationNeuralNetwork

     Neural network classification

     The ‘ClassificationNeuralNetwork’ class implements a neural network
     classifier object, which can predict responses for new data using the
     ‘predict’ method.

     Neural network classification is a machine learning method that uses
     interconnected nodes in multiple layers to learn complex patterns in data.
     It processes inputs through hidden layers with activation functions to
     produce classification outputs.

     Create a ‘ClassificationNeuralNetwork’ object by using the ‘fitcnet’
     function or the class constructor.

     See also: fitcnet.


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Neural network classification



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ClassificationPartitionedModel


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 -- statistics: ClassificationPartitionedModel

     Cross-validated classification model

     The ‘ClassificationPartitionedModel’ class stores cross-validated
     classification models trained on different partitions of the data.  It can
     predict responses for observations not used for training using the
     ‘kfoldPredict’ method.

     Create a ‘ClassificationPartitionedModel’ object by using the ‘crossval’
     function.

     See also: crossval.


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Cross-validated classification model



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ClassificationSVM


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 -- statistics: ClassificationSVM

     Support Vector Machine classification

     The ‘ClassificationSVM’ class implements a Support Vector Machine
     classifier object for one-class or two-class problems, which can predict
     responses for new data using the ‘predict’ method.

     Support Vector Machine classification is a supervised learning method used
     for classification tasks.  It works by finding the optimal hyperplane that
     separates classes in the feature space with the maximum margin.  For
     non-linearly separable data, it uses kernel functions to map data to a
     higher-dimensional space where separation is possible.

     Create a ‘ClassificationSVM’ object by using the ‘fitcsvm’ function or the
     class constructor.

     See also: fitcsvm.


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Support Vector Machine classification



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CompactClassificationDiscriminant


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 -- statistics: CompactClassificationDiscriminant

     Compact discriminant analysis classification

     The ‘CompactClassificationDiscriminant’ class implements a compact version
     of a linear discriminant analysis classifier object, which can predict
     responses for new data using the ‘predict’ method but does not store the
     training data.

     A ‘CompactClassificationDiscriminant’ object is a compact version of a
     discriminant analysis model, ‘ClassificationDiscriminant’.  It does not
     include the training data resulting in a smaller classifier size, which can
     be used for making predictions from new data, but not for tasks such as
     cross validation.  It can only be created from a
     ‘ClassificationDiscriminant’ model by using the ‘compact’ object method.

     Create a ‘CompactClassificationDiscriminant’ object by using the ‘compact’
     method of a ‘ClassificationDiscriminant’ object.

     See also: fitcdiscr, ClassificationDiscriminant.


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Compact discriminant analysis classification



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CompactClassificationGAM


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 -- statistics: CompactClassificationGAM

     Compact generalized additive model classification

     The ‘CompactClassificationGAM’ class is a compact version of a Generalized
     Additive Model classifier, ‘ClassificationGAM’.  It does not include the
     training data, resulting in a smaller classifier size that can be used for
     making predictions from new data, but not for tasks such as cross
     validation.

     A ‘CompactClassificationGAM’ object can only be created from a
     ‘ClassificationGAM’ model by using the ‘compact’ method.

     See also: ClassificationGAM, fitcgam.


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Compact generalized additive model classification



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CompactClassificationNeuralNetwork


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 -- statistics: CompactClassificationNeuralNetwork

     Compact neural network classification

     The ‘CompactClassificationNeuralNetwork’ class implements a compact version
     of the neural network classifier object, which can predict responses for
     new data using the ‘predict’ method, but does not store the training data.

     A compact neural network classification model is a smaller version of the
     full ‘ClassificationNeuralNetwork’ model that does not include the training
     data.  It consumes less memory than the full model, but cannot perform
     tasks that require the training data, such as cross-validation.

     Create a ‘CompactClassificationNeuralNetwork’ object by using the ‘compact’
     method on a ‘ClassificationNeuralNetwork’ object.

     See also: ClassificationNeuralNetwork, fitcnet.


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Compact neural network classification



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CompactClassificationSVM


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 -- statistics: CompactClassificationSVM

     Compact Support Vector Machine classification

     The ‘CompactClassificationSVM’ class implements a compact version of a
     Support Vector Machine classifier object for one-class or two-class
     problems, which can predict responses for new data using the ‘predict’
     method.

     A ‘CompactClassificationSVM’ object is a compact version of a support
     vector machine model, ‘ClassificationSVM’.  It does not include the
     training data resulting in a smaller classifier size, which can be used for
     making predictions from new data, but not for tasks such as cross
     validation.  It can only be created from a ‘ClassificationSVM’ model by
     using the ‘compact’ object method.

     See also: ClassificationSVM.


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Compact Support Vector Machine classification



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ConfusionMatrixChart


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 -- statistics: ConfusionMatrixChart

     Confusion matrix chart for classification results

     The ‘ConfusionMatrixChart’ class implements a confusion matrix chart
     object, which displays the classification performance of a classifier by
     showing the counts of true positive, true negative, false positive, and
     false negative predictions.

     A confusion matrix chart is a visual representation of the performance of a
     classification algorithm.  The rows represent the true classes and the
     columns represent the predicted classes.  The diagonal elements represent
     the correctly classified observations, while the off-diagonal elements
     represent the misclassified observations.

     Create a ‘ConfusionMatrixChart’ object by using the ‘confusionchart’
     function.

     See also: confusionchart.


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Confusion matrix chart for classification results





