PowerPoint Presentation · 2021. 1. 22. · alpha 目标 损失函数...
Transcript of PowerPoint Presentation · 2021. 1. 22. · alpha 目标 损失函数...
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Reference: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
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参数 类型 RandomForestClassifier RandomForestRegressor GradientBoostingClassifier GradientBoostingRegressor
loss 目标损失函数
● exponential:模型等同AdaBoost
★ deviance:和Logistic Regression的损失函数一致
损失函数● exponential:模型等同AdaBoost
★ deviance:和Logistic Regression的损失函数一致
alpha 目标 损失函数为huber或quantile的时,alpha为损失函数中的参数
损失函数为huber或quantile的时,alpha为损失函数中的参数
class_weight 目标 类别的权值
n_estimators 性能子模型的数量● int:个数★ 10:默认值
子模型的数量● int:个数★ 10:默认值
子模型的数量● int:个数★ 100:默认值
子模型的数量● int:个数★ 100:默认值
learning_rate 性能 学习率(缩减) 学习率(缩减)
criterion 性能判断节点是否继续分裂采用的计算方法
● entropy
★ gini
判断节点是否继续分裂采用的计算方法★ mse
max_features 性能
节点分裂时参与判断的最大特征数● int:个数
● float:占所有特征的百分比★ auto:所有特征数的开方● sqrt:所有特征数的开方
● log2:所有特征数的log2值● None:等于所有特征数
节点分裂时参与判断的最大特征数● int:个数
● float:占所有特征的百分比★ auto:所有特征数的开方● sqrt:所有特征数的开方
● log2:所有特征数的log2值● None:等于所有特征数
节点分裂时参与判断的最大特征数● int:个数
● float:占所有特征的百分比● auto:所有特征数的开方● sqrt:所有特征数的开方
● log2:所有特征数的log2值★ None:等于所有特征数
节点分裂时参与判断的最大特征数● int:个数
● float:占所有特征的百分比● auto:所有特征数的开方● sqrt:所有特征数的开方
● log2:所有特征数的log2值★ None:等于所有特征数
subsample 性能子采样率
● float:采样率★ 1.0:默认值
子采样率● float:采样率★ 1.0:默认值
init 性能 初始子模型 初始子模型
n_jobs 效率并行数
● int:个数● -1:跟CPU核数一致
★ 1:默认值
并行数● int:个数
● -1:跟CPU核数一致★ 1:默认值
warm_start 效率是否热启动,如果是,则下一次训练是以追加树的形式
进行● bool:热启动★ False:默认值
是否热启动,如果是,则下一次训练是以追加树的形式进行
● bool:热启动★ False:默认值
是否热启动,如果是,则下一次训练是以追加树的形式进行
● bool:热启动★ False:默认值
是否热启动,如果是,则下一次训练是以追加树的形式进行
● bool:热启动★ False:默认值
presort 效率是否预排序,预排序可以加速查找最佳分裂点,对于稀疏
数据不管用● Bool
★ auto:非稀疏数据则预排序,若稀疏数据则不预排序
是否预排序,预排序可以加速查找最佳分裂点,对于稀疏数据不管用● Bool
★ auto:非稀疏数据则预排序,若稀疏数据则不预排序
oob_score 附加 是否计算袋外得分★ False:默认值
是否计算袋外得分★ False:默认值
random_state 附加 随机器对象 随机器对象 随机器对象 随机器对象
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• DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, mi
n_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_l
eaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, preso
rt='deprecated', ccp_alpha=0.0)
• LinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_
intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter
=1000)
• GradientBoostingClassifier(*, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1
.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=
0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state
=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='depre
cated', validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)
• LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_sca
ling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', v
erbose=0, warm_start=False, n_jobs=None, l1_ratio=None)
• SGDRegressor(loss='squared_loss', *, penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True,
max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate
='invscaling', eta0=0.01, power_t=0.25, early_stopping=False, validation_fraction=0.1, n_iter_no_ch
ange=5, warm_start=False, average=False)
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生成超参数 中间结果
最终结果
是否提前结束
中间结果、最终结果:即模型的优化目标,如精度(Accuracy)、损失值(Loss)
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贝叶斯优化
其他优化算法
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随机
数值类型 支持所有数值类型
历史数据 不利用
开发与探索的平衡 相当于只探索,但无探索策略
搜索空间大小的适应 大搜索空间不易找到较优结果
其它特点 通常也能得出可接受的结果,可作为其它优化算法的基准
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遍历
数值类型 支持有限的数值类型,如枚举、整型、以及分段的浮点数
历史数据 不利用
开发与探索的平衡 尝试所有可能性
搜索空间大小的适应 搜索空间大小直接影响搜索时间
其它特点 仅适用于非常小的搜索空间
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从较高的初始温度开始,出发, 这个温度称为初始温度, 伴随着温度参数的不断下降, 算法中的解趋于稳定, 但是, 可能这样的稳定解是一个局部最优解, 此时, 模拟退火算法中会以一定的概率跳出这样的局部最优解, 以寻找目标函数的全局最优解. 这个概率就与温度参数的大小有关, 温度参数越大, 概率越大, 反之则越小.
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// From:https://www.cnblogs.com/heaad/archive/2010/12/20/1911614.html
/*
* J(y):在状态y时的评价函数值 Y(i):表示当前状态 Y(i+1):表示新的状态* r: 用于控制降温的快慢 T: 系统的温度,系统初始应该要处于一个高温的状态* T_min :温度的下限,若温度T达到T_min,则停止搜索*/
while( T > T_min )
{
dE = J( Y(i+1) ) - J( Y(i) ) ;
if ( dE >=0 ) //表达移动后得到更优解,则总是接受移动Y(i+1) = Y(i) ; //接受从Y(i)到Y(i+1)的移动
else
{
// dE < 0, 函数exp( dE/T )的取值范围是(0,1) ,dE/T越大,则exp( dE/T )也越大if ( exp( dE/T ) > random( 0 , 1 ) )
Y(i+1) = Y(i) ; //接受从Y(i)到Y(i+1)的移动}
T = r * T ; //降温退火 ,0<r<1 。r越大,降温越慢;r越小,降温越快/*
* 若r过大,则搜索到全局最优解的可能会较高,但搜索的过程也就较长。若r过小,则搜索的过程会很快,但最终可能会达到一个局部最优值
*/
i ++ ;
}
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模拟退火
数值类型 支持各种数据类型
历史数据 使用历史数据来确定最优结果及温度
开发与探索的平衡 初期趋于探索,后期趋于挖掘
搜索空间大小的适应 大搜索空间不易找到较优结果
其它特点
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https://www.cnblogs.com/heaad/archive/2010/12/23/1914725.html
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https://www.cnblogs.com/maybe2030/p/4665837.html#_label0
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其设计的好坏将直接决定整个算法性能的优劣
https://www.cnblogs.com/maybe2030/p/4665837.html#_label0
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进化
数值类型 支持各种数据类型
历史数据 使用历史数据来比较出较好的超参组合
开发与探索的平衡 初始种群越大,越倾向于探索。变异的超参数量越多、变化范围越大,越趋向于探索。
搜索空间大小的适应 需要较多迭代次数
其它特点
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Tree Parzen Estimators
贝叶斯优化
数值类型 支持各种数据类型,以及数据间的级联关系
历史数据 替代模型使用历史数据来评估搜索空间
开发与探索的平衡 采集函数用于平衡挖掘与探索
搜索空间大小的适应 对较大搜索空间也较有效
其它特点 特别适合解决自动机器学习这类主动学习的问题。如果并发数量较多,优化效率会下降。
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https://static.googleusercontent.com/media/research.google.com/en//
pubs/archive/46180.pdf
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http://aad.informatik.uni-freiburg.de/papers/15-IJCAI-
Extrapolation_of_Learning_Curves.pdf
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∈ A
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在一定原则下可以表示的结构。
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减少表示每个量化对象(例如:权重,激活函数,梯度)所需的比特数来压缩原始网络。
减少表示精度的同时,不降低模型的准确度
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https://nni.readthedocs.io
https://en.wikipedia.org/wiki/Simulated_annealing
https://zhuanlan.zhihu.com/p/86386926
http://krasserm.github.io/2018/03/21/bayesian-
optimization/
https://papers.nips.cc/paper/4443-
algorithms-for-hyper-parameter-optimization.pdf
https://arxiv.org/pdf/1808.05377.pdf
https://www.analyticsvidhya.com/blog/2016/12/introduction-to-
feature-selection-methods-with-an-example-or-how-to-select-the-right-
variables/