Sympathy
1.4
What is Sympathy for Data?
What’s new
Installation instructions
Deprecations
Getting started
The graphical user interface
Typical workflow structure
Concepts in Sympathy for Data
Machine Learning Concepts
Subflows
Lambda
Using Sympathy from the command line
Node writing
Advanced node writing
Debugging, profiling, and tests
How to create reusable nodes
Creating a custom data type
Using Interactive (Using the Library interactively)
Using and supporting Python 3
Node interface reference
Parameter helper reference
Data type APIs
Library
Internal
Sympathy
Data
Datasources
Examples
Export
Files
Filters
Imageprocessing
List
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
Score Cross Validation
Group K-fold Cross Validation
K-fold Cross Validation
Leave One Group out Cross Validation
Simple Train-Test Split
Split Data for Cross Validation
Stratified K-fold cross validation
Time Series K-fold Based Cross Validation
Decision Tree Classifier
Kernel Principal Component Analysis (KPCA)
Principal Component Analysis (PCA)
Multi-output classifier
Multi-output regressor
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
Random
Reporting
Selectors
Slice
Tuple
Visualize
Plugins
Sympathy
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Set Input and Output Names
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Set Input and Output Names
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