WSO2 ML uses the following algorithms to create models using the data in a give data set.
Algorithm  Description  Type  Supported Publish/Download Formats  Related Samples 

LINEAR REGRESSION  Linear Regression algorithm trains a Generalized Linear Model that contains a relationship between independent variables (feature values in data) and the dependent variable (response variable in the data).  Numerical prediction 
 
RIDGE REGRESSION  Ridge Regression algorithm is a variant of Linear Regression where the loss function is the linear least squares function and the regularization is L2.  Numerical prediction 
 
LASSO REGRESSION  Lasso Regression algorithm is a variant of Linear Regression trained with L1 prior as regularizer.  Numerical Prediction 
 
LOGISTIC REGRESSION  Logistic Regression algorithm is a Generalized Linear Model which predicts the probability of a binary outcome. Logistic function is used to determine the probabilities of the outcomes.  Binary Classfication 
 
Support Vector Machine  Support Vector Machine is a nonprobabilistic binary classifier. It constructs a hyperplane or set of hyperplanes in a high (or infinite) dimensional space which generates a good separation of data points between classes.  Binary classification 
 
LOGISTIC REGRESSION LBFGS  Binary logistic regression can be generalized into multinomial logistic regression to train and predict multiclass classification problems. For k number of classes, It treats the first class as one class and the rest of the k1 classes as another class and the class with the largest probability is chosen as the prediction. LBFGS (Limited memory BFGS) is used as an optimization technique for faster convergence.  Multiclass Classification 
 
DECISION TREE  Decision Tree algorithm creates a treelike model that predicts the value of a target variable by learning simple decision rules inferred from the features of the dataset.  Multiclass Classification 
 
RANDOM FOREST CLASSIFICATION  Random Forest Classification algorithm is an ensemble learning method which combines many decision trees in order to reduce the risk of overfitting. Different decision trees are trained with different bootstraps drawn from the dataset (both feature bootstrapping and data point bootstrapping). At prediction, majority vote is taken from the trained decision trees.  Multiclass Classification 
 
RANDOM FOREST REGRESSION  Random Forest Regression algorithm is an ensemble learning method which combines many decision tree regressors in order to reduce the risk of overfitting. Different decision tree regressors are trained with different bootstraps drawn from the dataset (both feature bootstrapping and data point bootstrapping). The value is predicted to be the average of the tree predictions.  Numerical Prediction 
 
NAIVE BAYES  Naive Bayes algorithm assumes the independence between every pair of features in the dataset. It computes the conditional probability distribution of each feature given the class label, and then it applies Bayes’ theorem to compute the conditional probability distribution of label given a data point and use it for prediction. Negative feature values are not allowed when training a Naive Bayes model.  Multiclass Classification 
 
KMEANS  KMeans algorithm partitions the data points into a predefined number of clusters (k) in which each data point belongs to the cluster with the nearest mean, serving as a representative (cluster center) of the cluster.  Clustering 
 
KMEANS WITH UNLABLED DATA  This is a stateofart algorithm which performs Kmeans clustering algorithm on the training data. Data points which are beyond the cluster boundaries (according to a specific percentile value) are detected as anomalies. Labeled data is not required.  Anomaly Detection 
 
KMEANS WITH LABLED DATA  This is a stateofart algorithm which performs Kmeans clustering algorithm on the training data. Data points which are beyond the cluster boundaries (according to a specific percentile value) are detected as anomalies. This is used when labels (normal and anomalous) are available.  Anomaly Detection 
 
STACKED AUTOENCODERS  Stacked Autoencoders algorithms is a multilayer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The nodes in the input layer represent the features in the dataset and the nodes in the output layer represent the class labels of the outcomes.  Deep Learning 
 
COLLABORATIVE FILTERING (Explicit Data)  Collaborative Filtering is used in recommendation systems and aims to fill in the missing entries of a useritem association matrix. This algorithm allows entries in the useritem matrix as explicit preferences(ratings) given by the user to the item. Recommendations are based on these explicitly rates.  Recommendation 
 
COLLABORATIVE FILTERING (Implicit Feedback Data)  Collaborative Filtering is used in recommendation systems and aims to fill in the missing entries of a useritem association matrix. This algorithm allows preferences on the products to be implicit feedbacks such as views, clicks, purchases, likes, shares etc. Recommendations are based on these implicit feedbacks.  Recommendation 

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