Linear model for binary classification of high-dimensional data
ClassificationLinear
is a trained linear model object for binary classification; the linear model is a support vector machine (SVM) or logistic regression model. fitclinear
fits a ClassificationLinear
model by minimizing the objective function using techniques that reduce computation time for high-dimensional data sets (e.g., stochastic gradient descent). The classification loss plus the regularization term compose the objective function.
Unlike other classification models, and for economical memory usage, ClassificationLinear
model objects do not store the training data. However, they do store, for example, the estimated linear model coefficients, prior-class probabilities, and the regularization strength.
You can use trained ClassificationLinear
models to predict labels or classification scores for new data. For details, see predict
.
Create a ClassificationLinear
object by using fitclinear
.
edge | Classification edge for linear classification models |
incrementalLearner | Convert linear model for binary classification to incremental learner |
lime | Local interpretable model-agnostic explanations (LIME) |
loss | Classification loss for linear classification models |
margin | Classification margins for linear classification models |
partialDependence | Compute partial dependence |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
predict | Predict labels for linear classification models |
shapley | Shapley values |
selectModels | Choose subset of regularized, binary linear classification models |
update | Update model parameters for code generation |
Value. To learn how value classes affect copy operations, see Copying Objects.
ClassificationECOC
| ClassificationKernel
| ClassificationPartitionedLinear
| ClassificationPartitionedLinearECOC
| fitclinear
| predict