4.3 决策树
输出
DecisionTreeClassifier 训练集得分(max_depth=1): 63.39% 测试集得分(max_depth=1):65.79% 训练集得分(max_depth=3): 86.61% 测试集得分(max_depth=3):71.05% 训练集得分(max_depth=5): 88.39% 测试集得分(max_depth=5):68.42%(过拟合) DecisionTreeRegressor 训练集得分(max_depth=1): 58.97% 测试集得分(max_depth=1):59.45%(欠拟合) 训练集得分(max_depth=3): 84.20% 测试集得分(max_depth=3):69.89%(过拟合) 训练集得分(max_depth=5): 88.19% 测试集得分(max_depth=5):62.75%(过拟合)
4.4 K邻近算法
输出
KNeighborsClassifier 训练集得分(KNeighborsClassifier): 87.50% 测试集得分(KNeighborsClassifier):68.42% KNeighborsRegressor 训练集得分(KNeighborsRegressor): 86.97% 测试集得分(KNeighborsRegressor):70.15%
4.5 贝叶斯算法
输出
BernoulliNB 训练集得分(BernoulliNB):33.93% 测试集得分(BernoulliNB):31.58%(欠拟合) GaussianNB 训练集得分(GaussianNB): 83.04% 测试集得分(GaussianNB):68.42% MultinomialNB 训练集得分(MultinomialNB): 66.07% 测试集得分(MultinomialNB):65.79%(欠拟合)
4.6 SVM
输出
SVC 训练集得分(kernel=linear): 83.04% 测试集得分(kernel=linear):71.05% 训练集得分(kernel=rbf): 83.93% 测试集得分(kernel=rbf):71.05% 训练集得分(kernel=sigmoid):33.93% 测试集得分(kernel=sigmoid):31.58%(欠拟合) 训练集得分(kernel=poly): 85.71% 测试集得分(kernel=poly):71.05% 训练集得分(gamma=0.1): 83.93% 测试集得分(gamma=0.1):71.05% 训练集得分(gamma=1): 84.82% 测试集得分(gamma=1):71.05% 训练集得分(gamma=10): 88.39% 测试集得分(gamma=10):68.42% 训练集得分(C=1.0): 83.93% 测试集得分(C=1.0):71.05% 训练集得分(C=3.0): 85.71% 测试集得分(C=3.0):68.42% 训练集得分(C=5.0): 84.82% 测试集得分(C=5.0):71.05% SVR 训练集得分(kernel=linear): 72.24% 测试集得分(kernel=linear):70.81% 训练集得分(kernel=rbf): 78.14% 测试集得分(kernel=rbf):67.68% 训练集得分(kernel=sigmoid):-0.00% 测试集得分(kernel=sigmoid):-0.00%(欠拟合) 训练集得分(kernel=poly): 74.86% 测试集得分(kernel=poly):63.64% 训练集得分(gamma=0.1): 77.03% 测试集得分(gamma=0.1):70.56% 训练集得分(gamma=1): 81.50% 测试集得分(gamma=1):64.05% 训练集得分(gamma=10): 86.63% 测试集得分(gamma=10):61.78% 训练集得分(C=1.0): 78.14% 测试集得分(C=1.0):67.68% 训练集得分(C=3.0): 78.90% 测试集得分(C=3.0):66.10% 训练集得分(C=5.0): 79.22% 测试集得分(C=5.0):65.32% LinearSVC 训练集得分(C=1.0): 83.93% 测试集得分(C=1.0):68.42% 训练集得分(C=3.0): 84.82% 测试集得分(C=3.0):68.42% 训练集得分(C=5.0): 83.93% 测试集得分(C=5.0):68.42% LinearSVR 训练集得分(C=1.0): 71.61% 测试集得分(C=1.0):70.75% 训练集得分(C=3.0): 72.21% 测试集得分(C=3.0):71.15% 训练集得分(C=5.0): 72.43% 测试集得分(C=5.0):71.37%
在SVC、SVR中,gamma越大,包围圈越小
4.7聚类
4.8 神经网络
输出
MLPClassifier 训练集得分(activation=relu): 86.61% 测试集得分(activation=relu):71.05% 训练集得分(activation=tanh): 87.50% 测试集得分(activation=tanh):71.05% 训练集得分(activation=identity): 85.71% 测试集得分(activation=identity):68.42%(过拟合) 训练集得分(activation=logistic): 91.07% 测试集得分(activation=logistic):68.42%(过拟合) 训练集得分(alpha=0.0001): 85.71% 测试集得分(alpha=0.0001):71.05% 训练集得分(alpha=0.001): 84.82% 测试集得分(alpha=0.001):71.05% 训练集得分(alpha=0.01):33.93% 测试集得分(alpha=0.01):31.58%(欠拟合) 训练集得分(alpha=1): 85.71% 测试集得分(alpha=1):71.05% MLPRegressor 训练集得分(activation=relu): 83.68% 测试集得分(activation=relu):70.17% 训练集得分(activation=tanh): 84.25% 测试集得分(activation=tanh):69.02% 训练集得分(activation=identity): 72.99% 测试集得分(activation=identity):71.12% 训练集得分(activation=logistic): 85.25% 测试集得分(activation=logistic):67.18% 训练集得分(alpha=0.0001): 83.99% 测试集得分(alpha=0.0001):70.05% 训练集得分(alpha=0.001): 72.99% 测试集得分(alpha=0.001):71.12% 训练集得分(alpha=0.01): 84.42% 测试集得分(alpha=0.01):68.65% 训练集得分(alpha=1): 83.20% 测试集得分(alpha=1):71.39%
4.9 集成学习
输出
AdaBoostClassifier 训练集得分(AdaBoostClassifier): 50.89% 测试集得分(AdaBoostClassifier):36.84% (欠拟合) AdaBoostRegressor 训练集得分(AdaBoostRegressor): 82.37% 测试集得分(AdaBoostRegressor):74.00% BaggingClassifier BaggingRegressor VotingClassifier 训练集得分(voting=hard): 84.82% 测试集得分(voting=hard):68.42% 训练集得分(voting=soft): 92.86% 测试集得分(voting=soft):68.42% (过拟合)
4.10 降维
输出
LinearDiscriminantAnalysis 训练集得分(LinearDiscriminantAnalysis):85.71% 测试集得分(LinearDiscriminantAnalysis):73.68%
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