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Parameter tuning for SVM using Grid Search
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| from sklearn.model_selection import GridSearchCV | |
| parameters = {'kernel':('linear', 'rbf'), 'C':[1,2,3,4,5,6,7,8,9,10], 'gamma': | |
| [0.01,0.02,0.03,0.04,0.05,0.10,0.2,0.3,0.4,0.5]} | |
| svr = svm.SVC() | |
| grid = GridSearchCV(svr, parameters) | |
| grid.fit(X_train, y_train) | |
| predicted = grid.predict(X_test) | |
| cnf_matrix = confusion_matrix(y_test, predicted) | |
| print(cnf_matrix) | |
| [[16 0 0] | |
| [ 0 13 5] | |
| [ 0 3 8]] |
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You are using too much values for each hyperparameter. The GridSearch will take forever to run even with a decent compute power.