Orange腳本調用Data Mining Library

原文（英）：http://docs.orange.biolab.si/3/data-mining-library/

Orange Data Mining Library

教程

這是一個有好的關於 Orange 腳本使用的教程, 而Orange是一個基於Python 3的數據挖掘支持庫。假定你已經下載和安裝了 Orange， 能夠從github repository 得到的版本，而且有一個能夠正常運行的Python環境，注意須要python3，python3.4將是目前推薦的版本。使用Python shell，輸入下面的代碼：

% python
>>> import Orange
>>> Orange.version.version
'3.2.dev0+8907f35'
>>>

若是沒有錯誤和警告信息，Orange 和 Python 就已經正確安裝，能夠繼續下面的教程。

• The Data

• Data Input

• Saving the Data

• Exploration of the Data Domain

• Data Instances

• Orange Data Sets and NumPy

• Meta Attributes

• Missing Values

• Data Selection and Sampling

• Classification

• Learners and Classifiers

• Probabilistic Classification

• Cross-Validation

• Handful of Classifiers

• Regression

• Handful of Regressors

• Cross Validation

參考

可用的類庫和方法：

• Data model (data)

• Data Storage (storage)

• Data Table (table)

• SQL table (data.sql)

• Domain description (domain)

• Variable Descriptors (variable)

• Values (value)

• Data Instance (instance)

• Data Filters (filter)

• Data Preprocessing (preprocess)

• Impute

• Discretization

• Continuization

• Normalization

• Randomization

• Remove

• Feature selection

• Preprocessors

• Classification (classification)

• Logistic Regression

• Random Forest

• Simple Random Forest

• Softmax Regression

• k-Nearest Neighbors

• Naive Bayes

• Multilayer perceptron (feed-forward neural network)

• Support Vector Machines

• Linear Support Vector Machines

• Nu-Support Vector Machines

• One Class Support Vector Machines

• Classification Tree

• Simple Tree

• Majority Classifier

• Elliptic Envelope

• Regression (regression)

• Linear Regression

• Mean

• Random Forest

• Simple Random Forest

• Regression Tree

• Clustering (clustering)

• Hierarchical (hierarchical)

• Distance (distance)

• Evaluation (evaluation)

• Sampling procedures for testing models (testing)

• Scoring methods (scoring)

• Miscellaneous (misc)

• Symbolic constants (enum)

• Distance Matrix (distmatrix)