- Documentation
- Studio
- Operator Manual
You are viewing the RapidMiner Studio documentation for version 9.0 - 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
- external_retrieve
- external_store
- move_repository_entry
- rename_repository_entry
- retrieve
- store
- Files
- Read
- Read SAS
- read_access
- read_arff
- read_bibtex
- read_c4.5
- read_csv
- read_dasylab
- read_dbase
- read_excel
- read_spss
- read_stata
- read_url
- read_xml
- read_xrff
- Write
- Database
- NoSQL
- Cassandra
- MongoDB
- Solr
- Applications
- Trigger Zapier
- Qlik
- Splunk
- Salesforce
- Mozenda
- 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 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
- Google BigQuery
- Google Sheets
- IoT
- Altair IoT
- Blending
- Python Transformer
- Attributes
- order_attributes
- Names & Roles
- Types
- One-Hot Encoding
- Target Encoding
- date_to_nominal
- date_to_numerical
- format_numbers
- guess_types
- nominal_to_binominal
- nominal_to_date
- nominal_to_numerical
- nominal_to_text
- numerical_to_binominal
- numerical_to_date
- numerical_to_polynominal
- numerical_to_real
- parse_numbers
- real_to_integer
- set_positive_value
- text_to_nominal
- Selection
- remove_attribute_range
- remove_correlated_attributes
- remove_useless_attributes
- select_attributes
- select_by_random
- select_by_weights
- work_on_subset
- Generation
- Generate Batch
- Text Vectorization
- generate_absolutes
- generate_aggregation
- generate_columns
- generate_concatenation
- generate_copy
- generate_empty_attribute
- generate_function_set
- generate_gaussians
- generate_id
- generate_item_set_indicators
- generate_products
- generate_tfidf
- generate_weight_lpr
- generate_weight_stratification
- Examples
- Filter
- Sampling
- Sort
- Table
- Grouping
- Rotation
- Joins
- Values
- Cleansing
- Quality Measures
- Statistics
- Normalization
- Binning
- discretize_by_bins
- discretize_by_entropy
- discretize_by_frequency
- discretize_by_size
- discretize_by_user_specification
- Missing
- Handle Unknown Values
- Replace All Missings
- declare_missing_value
- fill_data_gaps
- impute_missing_values
- remove_unused_values
- replace_infinite_values
- replace_missing_values
- Duplicates
- Outliers
- Dimensionality Reduction
- Modeling
- Python Forecaster
- Python Learner
- Predictive
- create_formula
- group_models
- ungroup_models
- update_model
- Lazy
- Bayesian
- Trees
- Gradient Boosted Trees
- chaid
- decision_stump
- decision_tree_multiway
- decision_tree_weight_based
- id3
- parallel_decision_tree
- parallel_random_forest
- random_tree
- Rules
- rule_induction
- single_rule_induction
- single_rule_induction_single_attribute
- subgroup_discovery
- tree_to_rules
- Neural Nets
- Functions
- Generalized Linear Model
- function_fitting
- gaussian_process
- 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
- Segmentation
- Cluster Model Visualizer
- agglomerative_clustering
- dbscan_apache
- extract_prototypes
- fast_k_means
- flatten_clustering
- k-Means (H2O)
- k_means
- k_means_kernel
- k_medoids
- random_clustering
- support_vector_clustering
- top_down_clustering
- x_means
- 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
- mututal_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_forest
- 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_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
- optimize_selection
- optimize_selection_backward
- optimize_selection_brute_force
- optimize_selection_evolutionary
- optimize_selection_forward
- 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
- 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
- Cost-Sensitive Scoring
- Explain Predictions
- Model Simulator
- Prescriptive Analytics
- apply_model
- Confidences
- Validation
- bootstrapping_validation
- cross_validation
- split_validation
- wrapper_split_validation
- wrapper_x_validation
- Performance
- Multi Label Performance
- combine_performances
- extract_performance
- performance
- performance_min_max
- performance_to_data
- performance_user_based
- 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
- Schedule Process
- execute_process
- multiply
- subprocess
- Scripting
- Process Control
- publish_to_app
- recall
- recall_from_app
- remember
- Loops
- loop
- loop_and_average
- loop_and_deliver_best
- loop_attribute_subsets
- loop_attributes
- loop_batches
- 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_zipfile_entries
- Branches
- Collections
- Exceptions
- Macros
- Files
- add_entry_to_archive_file
- copy_file
- create_archive_file
- create_directory
- delete_file
- move_file
- open_file
- rename_file
- write_as_text
- write_file
- write_message
- Annotations
- Logging
- 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
- MLFlow
- Experiments
- Models
- Text Processing
- Create Document
- Data to Documents
- Documents to Data
- Extract Document
- Process Documents
- Process Documents from Data
- Process Documents from Files
- Process 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)
- Filter Tokens (by Content)
- Filter Tokens (by Length)
- Filter Tokens (by POS Ratios)
- Filter Tokens (by POS Tags)
- Filter Tokens (by Region)
- Stemming
- Stem (Arabic)
- Stem (Arabic, Light)
- Stem (Dictionary)
- Stem (German)
- Stem (Lovins)
- Stem (Porter)
- Stem (Snowball)
- Transformation
- Web Mining
- Clear Cookies
- Crawl Web
- Get Page
- Get Pages
- Process Documents from Web
- Read RSS Feed
- Services
- Html Processing
- Utility
- Rest
- Admin Tools
- Data Access
- Ai Hub
- Add Contents to Project (AI Hub)
- Create Project (AI Hub)
- Delete Project (AI Hub)
- Delete Schedule (AI Hub)
- Get JWT (AI Hub)
- Get Jobs (AI Hub)
- Get Log (AI Hub)
- Get Metrics (AI Hub)
- Get Project (AI Hub)
- Get Projects (AI Hub)
- Get Schedules (AI Hub)
- Kill Job (AI Hub)
- Run Job (AI Hub)
- Schedule Job (AI Hub)
- Rtsa
- Image Processing
- Transformations
- Align Image
- Apply Threshold
- Blur Image
- Color Scale Image
- Convert Color
- Crop Image
- Dilate Image
- Erode Image
- Flip Image
- Generate Pixel Aggregation
- Invert Image
- Resize Image
- Rotate Image
- Translate Image
- Data Access
- Extract Image Information
- Image to Table
- Process Images from Webcam
- Read Image
- Read Image from Webcam
- Read Video
- Table to Image
- Write Image
- Regions
- Other
- Custom Operators
- Parameter Helper
- Attribute Parameter Macro
- Boolean Parameter Macro
- Category Parameter Macro
- Directory Parameter Macro
- Double Parameter Macro
- File Parameter Macro
- Integer Parameter Macro
- Repository Location Parameter Macro
- Text Parameter Macro
- Data Access
- Execute Process from Custom Extension
- Open File from Custom Extension
- Read Dictionary
- Retrieve from Custom Extension
- Progress
- Utility
- Generative AI
- In-Database Processing
- In Database Nest
- Data Access
- Blending
- Aggregate (In Database)
- Convert Type (In Database)
- Custom Query (In Database)
- Filter Example Range (In Database)
- Filter Examples (In Database)
- Generate Attributes (In Database)
- Generate Rank (In Database)
- Join (In Database)
- Rename (In Database)
- Rename by Replacing (In Database)
- Reorder Attributes (In Database)
- Replace (In Database)
- Sample (In Database)
- Select Attributes (In Database)
- Sort (In Database)
- Union (In Database)
- Cleansing
- Declare Missing Value (In Database)
- Remove Duplicates (In Database)
- Replace Missing Values (In Database)
- Utility
- 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)
- Filter Tokens Using ExampleSet
- Split Document into Collection
- Stem Tokens Using ExampleSet
- Utility
- Deployment