Orange Data Mining Library¶
Tutorial¶
This is a gentle introduction on scripting in Orange , a Python 3 data mining library. We here assume you have already downloaded and installed Orange from its github repository and have a working version of Python. In the command line or any Python environment, try to import Orange. Below, we used a Python shell:
% python
>>> import Orange
>>> Orange.version.version
'3.25.0.dev0+3bdef92'
>>>
If this leaves no error and warning, Orange and Python are properly installed and you are ready to continue with the tutorial.
Reference¶
Available classes and methods.
- Data model (
data
) - Data Preprocessing (
preprocess
) - Outlier detection (
classification
) - Classification (
classification
)- Logistic Regression
- Random Forest
- Simple Random Forest
- Softmax Regression
- k-Nearest Neighbors
- Naive Bayes
- Support Vector Machines
- Linear Support Vector Machines
- Nu-Support Vector Machines
- Classification Tree
- Simple Tree
- Majority Classifier
- Neural Network
- CN2 Rule Induction
- Calibration and threshold optimization
- Gradient Boosted Trees
- Regression (
regression
) - Clustering (
clustering
) - Distance (
distance
) - Evaluation (
evaluation
) - Projection (
projection
) - Miscellaneous (
misc
)