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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





