jlovedragon / svmfirstlook

some execrises about SVM, scikit-learn

#SVM

using scikit-learn do execrises about SVM

environment:Ubuntu14.04 sublime2 python2.7.6

Classification contrast :

• Linear model: compare with Perceptron

  • plot_linear_model.py
  • plot_perceptron_test1.py (different coef_init and intercept_init)
  • plot_perceptron_test2.py (different order)

• Non-linear model: To explain the performance of the svm is sensitive to the different kernel:

  • plot_svm_kernel.py (come from http://scikit-learn.org/)
  • plot_balancescale_test.py

Regression contrast :

• (RBF, linear, poly): plot_svm_regression.py (come from http://scikit-learn.org/)

How to choose parameters C and gamma in linear and RBF kernel ?

• plot_linear_parameters_different_c.py
• plot_linear_parameters_different_c_one_noise.py
• plot_rbf_parameters_different_c.py
• plot_rbf_parameters_different_gamma.py

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DataSets :

• iris

  • Number of Instances: 150 (50 in each of three classes)
  • Number of Attributes: 4 numeric, predictive attributes and the class

  Summary Statistics: Min Max Mean SD Class Correlation sepal length = 4.3 7.9 5.84 0.83 0.7826 sepal width = 2.0 4.4 3.05 0.43 -0.4194 petal length = 1.0 6.9 3.76 1.76 0.9490 (high!) petal width = 0.1 2.5 1.20 0.76 0.9565 (high!)

• balance scale (polynomial)

  • Number of Instances: 625 (49 balanced, 288 left, 288 right)
  • Number of Attributes: 4 (numeric) + class name = 5

  Later, I have already discarded the datas of 49 balanced to hold the same weight of the dataset.

##关于作者

    nickName = "Quentin_Hsu"
    email    = "[email protected]"