Sympathy
  • What is Sympathy for Data?
  • What’s new
    • News in 1.4.2
    • News in 1.4.1
    • News in 1.4.0
    • News in 1.3.5
    • News in 1.3.4
    • News in 1.3.3
    • News in 1.3.2
    • News in 1.3.1
    • News in 1.3 series
    • News in 1.2 series
  • Installation instructions
    • For Windows
    • For Linux
  • Deprecations
  • Getting started
    • Working with nodes and workflows
    • Executing a node and looking at the result
    • Loading and executing a workflow
    • Configuring nodes
  • The graphical user interface
    • The node library window
    • Error window
    • Flow overview
    • Undo stack
    • Data viewer
  • Typical workflow structure
    • Importing data
    • Prepare data
    • Analyze data
    • Export data as plots or reports
    • Working with ADAF
    • Control structures
  • Concepts in Sympathy for Data
    • Workflows
    • Nodes
    • Data types
    • Control structures
  • Machine Learning Concepts
    • Pre-processing data
    • Varying number of parameters
    • Pipelines
    • More (machine) learning
  • Subflows
    • Adding ports
    • Subflow settings
    • Subflow configuration
    • Linked Subflows
    • Locked Subflows
  • Functions
    • Lambda function
  • Using Sympathy from command line
    • Sympathy Start options
    • launch.py Start options
    • Using environment variables
    • Using config files
  • Frequently asked questions
    • How to add a third-party library
  • Node writing tutorial
    • Creating a library structure
    • The node wizard
    • The node code
    • Library tags
    • Adding input and output ports
    • Adding a configuration GUI
    • Errors and warnings
  • Advanced node writing
    • Adjust parameters
    • Controllers
    • Using custom port types
    • Managing node updates
    • Custom GUIs
  • Debugging, profiling and tests
    • Debugging nodes
    • Profiling nodes and workflows
    • Writing tests for your nodes
  • How to create reusable nodes
    • Add extra modules to your library
    • Library compatibility between 1.2 and 1.3
  • Creating a custom data type
    • Create typeutils class
    • Locate port type
    • Create port type
    • Create an example node
    • Adding an icon
    • Extend the data viewer
  • Using Interactive (Using the Library interactively)
    • Loading the Library
    • Loading nodes
    • Working with configurations
    • Working with nodes
  • Using and supporting Python 3
    • Python 3: differences compared to 2
    • Python 2 and 3 compatbility
    • Python 3 only
  • Node interface reference
    • Node definition
    • Overridable node methods
    • Callable node methods
    • Node context reference
  • Parameter helper reference
    • Adding scalar parameters
    • Adding lists
    • Adding groups and pages
    • Editors
  • Data type APIs
    • Table API
    • ADAF API
    • Datasource API
    • Text API
    • Figure API
    • Matlab API
  • Library
    • Internal
    • Sympathy
 
Sympathy
  • Docs »
  • Library »
  • Sympathy »
  • Machinelearning
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Machinelearning¶

  • Decision Function
  • Fit
  • Fit Texts
  • Fit Transform
  • Fit Transform Text
  • Inverse Transform
  • Predict
  • Predict Probabilities
  • Score
  • Select Features from Model
  • Transform
  • Transform Text
  • Extract Attributes
  • K-means Clustering
  • Mini-batch K-means Clustering
  • Group K-fold Cross Validation
  • K-fold Cross Validation
  • Leave One Group out Cross Validation
  • Simple Train-Test Split
  • Stratified K-fold cross validation
  • Time Series K-fold Based Cross Validation
  • Score Cross Validation
  • Split Data for Cross Validation
  • Decision Tree Classifier
  • Kernel Principal Component Analysis (KPCA)
  • Principal Component Analysis (PCA)
  • Voting Classifier
  • Example datasets
  • Export Model
  • Import Model
  • Generate dataset blobs
  • Generate dataset blobs from table
  • Generate classification dataset
  • Isolation Forest
  • Conditional Probabilty from Categories
  • Confusion Matrix
  • Learning Curve
  • ROC from Probabilities
  • Multi-Layer Perceptron Classifier
  • Extract Parameters
  • Parameter Distribution
  • Set Input and Output Names
  • Set Parameters
  • Grid Parameter Search
  • Randomized Parameter Search
  • Simulated Annealing Parameter Search
  • Pipeline
  • Pipeline decomposition
  • Binarizer
  • Categorical Encoder
  • Imputer
  • Label Binarizer
  • Label Encoder
  • Max Abs Scaler
  • Normalizer
  • One-Hot Encoder
  • Polynomial Features
  • Robust Scaler
  • Standard Scaler
  • Random Forest Classifier
  • Kernel Ridge Regression
  • Linear Regression
  • Logistic Regression
  • Epsilon Support Vector Regression
  • One Class SVM
  • Support Vector Classifier
  • Text Count Vectorizer
  • Features to Images
  • Images to Features
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