You are viewing the RapidMiner Studio documentation for version 9.7 -Check here for latest version
Operators
This page is also available as printer-friendly document:RapidMiner Operator Reference (PDF)
- Data Access
- Copy Repository Entry
- Delete Repository Entry
- Move Repository Entry
- Rename Repository Entry
- Retrieve
- Store
- Files
- Read
- Read ARFF
- Read Access
- Read BibTeX
- Read C4.5
- Read CSV
- Read DBase
- Read DasyLab
- Read Excel
- Read SAS
- Read SPSS
- Read Stata
- Read URL
- Read XML
- Read XRFF
- Write
- Database
- NoSQL
- Cassandra
- MongoDB
- Solr
- Applications
- Trigger Zapier
- Salesforce
- Mozenda
- Qlik
- Splunk
- Cloud Storage
- Amazon S3
- Azure Blob Storage
- Delete Azure Blob Storage Resource
- Loop Azure Blob Storage
- Read Azure Blob Storage
- Write Azure Blob Storage
- Azure Data Lake Storage Gen1
- Delete Azure Data Lake Storage Resource
- Loop Azure Data Lake Storage
- Read Azure Data Lake Storage
- Write Azure Data Lake Storage
- Azure Data Lake Storage Gen2
- Delete Azure Data Lake Storage Gen2 Resource
- Loop Azure Data Lake Storage Gen2
- Read Azure Data Lake Storage Gen2
- Write Azure Data Lake Storage Gen2
- Dropbox
- Google Storage
- Google Drive
- Blending
- Python Transformer
- Attributes
- Reorder Attributes
- Names & Roles
- Types
- Date to Nominal
- Date to Numerical
- Format Numbers
- Guess Types
- Nominal to Binominal
- Nominal to Date
- Nominal to Numerical
- 名义上的文本
- Numerical to Binominal
- Numerical to Date
- Numerical to Polynominal
- Numerical to Real
- One-Hot Encoding
- Parse Numbers
- Real to Integer
- Set Positive Value
- Target Encoding
- Text to Nominal
- Selection
- Remove Attribute Range
- 删除相关属性
- Remove Useless Attributes
- Select Attributes
- Select by Random
- Select by Weights
- Work on Subset
- Generation
- Generate Absolutes
- Generate Aggregation
- Generate Attributes
- Generate Batch
- Generate Concatenation
- Generate Copy
- Generate Empty Attribute
- Generate Function Set
- Generate Gaussians
- Generate ID
- Generate Item Set Indicators
- Generate Products
- Generate TFIDF
- 生成的重量(LPR)
- 生成的重量(Stratification)
- Text Vectorization
- Examples
- Filter
- Sampling
- Sample
- Sample (Bootstrapping)
- Sample (Kennard-Stone)
- Sample (Model-Based)
- Sample (Stratified)
- Split Data
- Sort
- Table
- Grouping
- Rotation
- Joins
- Values
- Cleansing
- Quality Measures
- Statistics
- Normalization
- De-Normalize
- Normalize
- Scale by Weights
- Binning
- Discretize by Binning
- Discretize by Entropy
- Discretize by Frequency
- Discretize by Size
- Discretize by User Specification
- Missing
- Declare Missing Value
- Fill Data Gaps
- Handle Unknown Values
- Impute Missing Values
- Remove Unused Values
- Replace All Missings
- 取代无限的价值
- Replace Missing Values
- Duplicates
- Outliers
- Dimensionality Reduction
- Modeling
- Python Forecaster
- Python Learner
- Predictive
- Create Formula
- Group Models
- Ungroup Models
- Update Model
- Lazy
- Bayesian
- Trees
- CHAID
- Decision Stump
- Decision Tree
- Decision Tree (Multiway)
- Decision Tree (Weight-Based)
- Gradient Boosted Trees
- ID3
- Random Forest
- Random Tree
- Rules
- Rule Induction
- Single Rule Induction
- Single Rule Induction (Single Attribute)
- Subgroup Discovery
- Tree to Rules
- Neural Nets
- AutoMLP
- Deep Learning
- Neural Net
- Perceptron
- Functions
- Function Fitting
- Gaussian Process
- Generalized Linear Model
- Linear Regression
- Local Polynomial Regression
- Polynomial Regression
- Relevance Vector Machine
- Seemingly Unrelated Regression
- Vector Linear Regression
- Logistic Regression
- Support Vector Machines
- Fast Large Margin
- Hyper Hyper
- Support Vector Machine
- Support Vector Machine (Evolutionary)
- Support Vector Machine (LibSVM)
- Support Vector Machine (Linear)
- Support Vector Machine (PSO)
- Discriminant Analysis
- Ensembles
- AdaBoost
- Additive Regression
- Bagging
- Bayesian Boosting
- Classification by Regression
- Find Threshold (Meta)
- Hierarchical Classification
- MetaCost
- Multi Label Modeling
- Polynominal by Binominal Classification
- Relative Regression
- Stacking
- Subgroup Discovery (Meta)
- Transformed Regression
- Vote
- Segmentation
- Agglomerative Clustering
- Cluster Model Visualizer
- DBSCAN
- Extract Cluster Prototypes
- Flatten Clustering
- Random Clustering
- Support Vector Clustering
- Top Down Clustering
- X-Means
- k-Means
- k-Means (H2O)
- k-Means (Kernel)
- k-Means (fast)
- k-Medoids
- Associations
- Apply Association Rules
- Create Association Rules
- FP-Growth
- Generalized Sequential Patterns
- Item Sets to Data
- Unify Item Sets
- Correlations
- ANOVA Matrix
- Correlation Matrix
- Covariance Matrix
- Grouped ANOVA
- Mutual Information Matrix
- Rainflow Matrix
- Transition Graph
- Transition Matrix
- Similarities
- Feature Weights
- Data to Weights
- Weight by Chi Squared Statistic
- Weight by Component Model
- Weight by Correlation
- Weight by Deviation
- Weight by Gini Index
- Weight by Information Gain
- Weight by Information Gain Ratio
- Weight by PCA
- Weight by Relief
- Weight by Rule
- Weight by SVM
- Weight by Tree Importance
- Weight by Uncertainty
- Weight by User Specification
- Weight by Value Average
- Weights to Data
- Optimization
- Apply Feature Set
- Automatic Feature Engineering
- Unsupervised Feature Selection
- Parameters
- Clone Parameters
- Optimize Parameters (Evolutionary)
- Optimize Parameters (Grid)
- Optimize Parameters (Quadratic)
- Set Parameters
- Feature Selection
- Backward Elimination
- Forward Selection
- Optimize Selection
- Optimize Selection (Brute Force)
- Optimize Selection (Evolutionary)
- Optimize Selection (Weight-Guided)
- Feature Generation
- Optimize by Generation (AGA)
- Optimize by Generation (Evolutionary Aggregation)
- Optimize by Generation (GGA)
- Optimize by Generation (YAGGA)
- Optimize by Generation (YAGGA2)
- Feature Weighting
- Optimize Weights (Backward)
- Optimize Weights (Evolutionary)
- Optimize Weights (Forward)
- Optimize Weights (PSO)
- Time Series
- Transformation
- Autocorrelation / Autocovariance
- Differentiate
- Equalize Numerical Indices
- Equalize Time Stamps
- Exponential Smoothing
- Fast Fourier Transformation
- Highest Peak Transformation
- Integrate
- Lag
- Logarithm
- Moving Average Filter
- Normalize (Series)
- Replace Missing Values (Series)
- Z-Score Peak Transformation
- Decomposition
- Feature Extraction
- Windowing
- Forecasting
- ARIMA
- Apply Forecast
- Default Forecast
- Function and Seasonal Component Forecast
- Holt-Winters
- Multi Horizon Forecast
- Validation
- Utility
- Scoring
- Apply Model
- Cost-Sensitive Scoring
- Explain Predictions
- Model Simulator
- Prescriptive Analytics
- Confidences
- Apply Threshold
- Create Threshold
- Drop Uncertain Predictions
- Find Threshold
- Generate Prediction
- Generate Prediction Ranking
- Rescale Confidences (Logistic)
- Select Recall
- Validation
- Bootstrapping Validation
- Cross Validation
- Split Validation
- Wrapper Split Validation
- Wrapper-X-Validation
- Performance
- 结合表演
- Extract Performance
- Multi Label Performance
- Performance
- Performance (Min-Max)
- Performance (User-Based)
- Performance to Data
- Predictive
- Performance (Attribute Count)
- Performance (Binominal Classification)
- Performance (Classification)
- Performance (Costs)
- Performance (Ranking)
- Performance (Regression)
- Performance (Support Vector Count)
- Segmentation
- Cluster Count Performance
- Cluster Density Performance
- Cluster Distance Performance
- Item Distribution Performance
- Map Clustering on Labels
- Significance Tests
- Visual
- Utility
- Execute Process
- Multiply
- Schedule Process
- Subprocess
- Scripting
- Process Control
- Publish to App
- Recall
- Recall from App
- Remember
- Loops
- Loop
- Loop Attribute Subsets
- Loop Attributes
- 循环批次
- Loop Clusters
- Loop Collection
- Loop Data Fractions
- Loop Data Sets
- Loop Examples
- Loop Files
- Loop Labels
- Loop Parameters
- Loop Repository
- Loop Until
- Loop Values
- Loop Zip-File Entries
- Loop and Average
- Loop and Deliver Best
- Branches
- Collections
- Exceptions
- Macros
- Extract Macro
- Generate Macro
- Set Macro
- 设置宏
- Unset Macro
- Files
- Add Entry to Archive File
- Copy File
- Create Archive File
- Create Directory
- Delete File
- Move File
- Open File
- Rename File
- Write File
- Write Message
- Write as Text
- Annotations
- Logging
- Clear Log
- Extract Log Value
- Log
- Log to Data
- Log to Weights
- Print to Console
- Provide Macro as Log Value
- Data Anonymization
- Random Data Generation
- Add Noise
- Create ExampleSet
- Generate Churn Data
- Generate Data
- Generate Direct Mailing Data
- Generate Massive Data
- Generate Multi-Label Data
- Generate Nominal Data
- Generate Sales Data
- Generate Team Profit Data
- Generate Transaction Data
- Generate Transfer Data
- Generate Up-Selling Data
- Misc
- Extensions
- Kafka Connector
- Operator Toolbox
- Blending
- Build Simulation
- Extract Statistics
- Filter Attributes with Missing Values
- Filter Examples with Missing Values
- Generate Aggregation (Advanced)
- Generate Partial Dependency Plot Data
- Get Holidays
- Rename by Multiple Examples
- Replace Rare Values
- SMOTE Upsampling
- Weight of Evidence
- Table
- Append (Superset)
- Collect and Persist
- Fuzzy Matching
- Group Into Collection
- Merge Attributes
- Sample (Collection)
- Sort (Multiple)
- Attribute Generation
- Data Access
- Data Export
- Feature Selection
- Macros
- Models
- Apply Association Rules (Detailed)
- Check Model Conformance
- GLM Contribution
- Get Decision Tree Path
- Local Interpretation (LIME)
- Optimize Threshold
- Optimize Threshold (Subprocess)
- Random Forest Encoder
- Outliers
- Parameters
- Performance
- Text Processing
- Apply Model (Documents)
- Dictionary-Based Sentiment (Documents)
- Extract Sentiment
- Extract Topics from Data (LDA)
- Extract Topics from Documents (LDA)
- 过滤器Tokens Using ExampleSet
- Split Document into Collection
- Stem Tokens Using ExampleSet
- Utility
- Text Processing
- Create Document
- Data to Documents
- Documents to Data
- Extract Document
- 过程Documents
- 过程Documents from Data
- 过程Documents from Files
- 过程Documents from Mail Store
- Read Document
- Read Documents (Mail)
- Write Document
- Generation
- Utility
- Tokenization
- Extraction
- Filtering
- Filter Documents (by Content)
- Filter Stopwords (Arabic)
- Filter Stopwords (Czech)
- Filter Stopwords (Dictionary)
- Filter Stopwords (English)
- Filter Stopwords (French)
- Filter Stopwords (German)
- 过滤器Tokens (by Content)
- 过滤器Tokens (by Length)
- 过滤器Tokens (by POS Ratios)
- 过滤器Tokens (by POS Tags)
- 过滤器Tokens (by Region)
- Stemming
- Stem (Arabic)
- Stem (Arabic, Light)
- Stem (Dictionary)
- Stem (German)
- Stem (Lovins)
- Stem (Porter)
- Stem (Snowball)
- Transformation
- Web Mining
- Deployment
