Generate a nonlinear classifier with Gaussian kernel function. First, generate one class of points inside the unit disk in two dimensions, and another class of points in the annulus from
Perform linear and quadratic classification of Fisher iris data.
Use a random subspace ensemble to increase the accuracy of classification. It also shows how to use cross validation to determine good parameters for both the weak learner template and the
You can also use ensembles of decision trees for classification. For this example, use ionosphere data with 351 observations and 34 real-valued predictors. The response variable is
Create a classification tree ensemble for the ionosphere data set, and use it to predict the classification of a radar return with average measurements.
When you have missing data, trees and ensembles of trees give better predictions when they include surrogate splits. Furthermore, estimates of predictor importance are often different
Obtain the benefits of the LPBoost and TotalBoost algorithms. These algorithms share two beneficial characteristics:
The RobustBoost algorithm can make good classification predictions even when the training data has noise. However, the default RobustBoost parameters can produce an ensemble that does
Make a more robust and simpler model by trying to remove predictors without hurting the predictive power of the model. This is especially important when you have many predictors in your data.
Predict posterior probabilities of SVM models over a grid of observations, and then plot the posterior probabilities over the grid. Plotting posterior probabilities exposes decision
Determine which quadrant of an image a shape occupies by training an error-correcting output codes (ECOC) model comprised of linear SVM binary learners. This example also illustrates the
Train a basic discriminant analysis classifier to classify irises in Fisher's iris data.
Train an ensemble of classification trees using data containing predictors with many categorical levels.
Perform classification using discriminant analysis, naive Bayes classifiers, and decision trees. Suppose you have a data set containing observations with measurements on different
Use a custom kernel function, such as the sigmoid kernel, to train SVM classifiers, and adjust custom kernel function parameters.
Train an ensemble of classification trees with unequal classification costs. This example uses data on patients with hepatitis to see if they live or die as a result of the disease. The data
Tune the regularization parameter in fscnca using cross-validation. Tuning the regularization parameter helps to correctly detect the relevant features in the data.
Optimize an SVM classification using the bayesopt function. The classification works on locations of points from a Gaussian mixture model. In The Elements of Statistical Learning ,
Optimize an SVM classification using the fitcsvm function and OptimizeHyperparameters name-value pair. The classification works on locations of points from a Gaussian mixture model. In
Visualize posterior classification probabilities predicted by a naive Bayes classification model.
Perform five-fold cross validation of a quadratic discriminant analysis classifier.
Plot the decision surface of different classification algorithms.
Classify when one class has many more observations than another. Try the RUSBoost algorithm first, because it is designed to handle this case.
Demonstrates fitting a non-linear regression tree model to hourly day-ahead electricity prices in the New England pool region. The log electricity prices are modeled with two additive