#! /usr/bin/env python3 from classifier import classifier_report from sklearn.neighbors import KNeighborsClassifier neighbors = range(1, 8) for n in neighbors: print('----------' + str(n) + ' neighbors ----------') classifier_report(KNeighborsClassifier(n_neighbors=n))
#! /usr/bin/env python3 from classifier import classifier_report from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier # Decission tree print('Decision tree:') classifier_report(AdaBoostClassifier(DecisionTreeClassifier())) # Decission stumps print('Decision stump:') classifier_report(AdaBoostClassifier(DecisionTreeClassifier(max_depth=1)))
#! /usr/bin/env python3 from classifier import classifier_report from sklearn.naive_bayes import BernoulliNB classifier_report(BernoulliNB())
#! /usr/bin/env python3 from classifier import classifier_report from sklearn.tree import DecisionTreeClassifier classifier_report(DecisionTreeClassifier(max_depth=10))
#! /usr/bin/env python3 from classifier import classifier_report from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier # Decission tree print('Decision tree:') classifier_report(BaggingClassifier(base_estimator=DecisionTreeClassifier())) # Decission stumps print('Decision stump:') classifier_report(BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),n_estimators=100))
#! /usr/bin/env python3 from classifier import classifier_report from sklearn.neighbors import KNeighborsClassifier neighbors = range(1,8) for n in neighbors: print('----------' + str(n) + ' neighbors ----------') classifier_report(KNeighborsClassifier(n_neighbors=n))
#! /usr/bin/env python3 from classifier import classifier_report from sklearn.svm import SVC # Polykernel print('Testing Polykernel') for d in range(1, 4): for c in range(-2, 3): cost = 10**c print('starting: degree = {0}, cost = {1}'.format(d, cost)) classifier_report(SVC(kernel='poly', degree=d, C=cost)) print('finished: degree = {0}, cost = {1}'.format(d, cost)) # Gaussian RBF Kernel print('Testing RBF') for g in range(-2, 3): for c in range(-2, 3): cost = 10**c gamma = 10**g print('starting: gamma = {0}, cost = {1}'.format(gamma, cost)) classifier_report(SVC(kernel='rbf', gamma=gamma, C=cost)) print('finished: gamma = {0}, cost = {1}'.format(gamma, cost))