| Class | Description |
|---|---|
| GaussianProcesses |
* Implements Gaussian processes for regression without hyperparameter-tuning.
|
| LinearRegression |
Class for using linear regression for prediction.
|
| Logistic |
Class for building and using a multinomial logistic
regression model with a ridge estimator.
There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables. |
| MultilayerPerceptron |
A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
|
| SGD |
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
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| SGDText |
Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
|
| SGDText.Count | |
| SimpleLinearRegression |
Learns a simple linear regression model.
|
| SimpleLogistic |
Classifier for building linear logistic regression
models.
|
| SMO |
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones. |
| SMOreg |
SMOreg implements the support vector machine for regression.
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| VotedPerceptron |
Implementation of the voted perceptron algorithm by Freund and Schapire.
|