RapidMiner (formerly YALE) and its plugins provide more than 400 operators for all aspects of Data Mining. Meta operators automatically optimize the experiment designs and users no longer need to tune single steps or parameters any longer. A huge amount of visualization techniques and the possibility to place breakpoints after each operator give insight into the success of your design - even online for running experiments. On this page we discuss the main groups of operators and give operator examples for each of the groups.

RapidMiner provides operators for:

	In- and output: flexible operators for data in- and output in different file formats including Known data mining and learning scheme formats (Arff, C4.5, csv ...) Sparse file formats (known from SVMlight, mySVM ...) Excel files SPSS files Data sets from databases (Oracle, mySQL, PostgreSQL, Microsoft SQL Server, Sybase ...) dBase Text files (Word Vector plugin) and Audio files (Value Series Plugin) and more
	Machine learning algorithms: more than 100 learning schemes for regression, classification, and clustering tasks, including: Support Vector Machines (SVM, LibSVM, SMO, mySVM ...) Decision Tree and Rule Learners (ID3, C4.5, PART, PRISM, RIPPER ...) Lazy Learners (Nearest Neighbors, K*, LBR ...) Bayesian Learners (Naive Bayes, Bayes Net, AODE ...) Logistic Learners (Logistic Regression, SimpleLogistic ...) Gaussian Processes Meta Learning (AdaBoost, Bagging, Stacking, BayesianBoosting ...) Association Rule Mining (Apriori, Tertius ...) Clustering (Clustering Plugin: k-Means, k-Medoids, DBscan, SVClustering ...) and more
	Weka operators: all learning schemes and attribute evaluators of the Weka learning environment are also available and can be used like all other RapidMiner operators
	Data preprocessing: operators which often have to be applied before the learning process include Discretization (Binning, Frequency ...) Example and feature filtering (Conditioned, ValueTypeFilter ...) Normalization (Interval, Standardization, z-Transformation ...) Sampling (Simple, Stratified, ModelBased ...) Dimensionality Reduction (PCA, Kernel-PCA, GHA, ICA ...) Missing and infinite value replenishment Removal of useless features and more
	Feature operators: Feature Selection (Forward Selection, Backward Elimination, Genetic Algorithms, WeightGuided ...) Feature Weighting and Relevance (ChiSquared, Correlation, InfoGain, RelieF ...) Feature Construction (GGA, YAGGA ...) Feature Extraction from time series (Value Series Plugin) and more
	Performance evaluation: several validation and evaluation schemes to estimate the performance of learning or preprocessing on your dataset, e.g. Cross-validation (stratified, shuffled, non-shuffled ...) Training and test set splitting (random, fixed ...) Leave-one-out Significance tests (ANOVA, paired t-Tests ...) Large number performance criteria for classification and regression (absolute, relative, accuracy, precision, recall, kappa, AUC ...) and more
	Meta operators: several optimization operators for experiment design, e.g. Parameter Optimization (Grid, Quadratic, Evolutionary ...) Learning Curves Experiment loops and iterations and more
	Visualization: logging and presenting results include the visualization of Online 1D, 2D and 3D plots of your data and experiment results (ExperimentLog, DataView, MetaDataView ...) Built-in color, histogram, and distribution plots Quartile / box plots Learned Models (Tree View, ClusterModel graphs ...) High-dimensional data (Andrew's Curves, GridViz, Parallel, RadViz, Survey, SOM ...) SVM functions (HyperplaneProjection, AttributeFunction ...) ROC plots Lift Charts and more