43
个编辑
更改
无编辑摘要
Automated machine learning can target various stages of the machine learning process. Essentially the targets can be grouped into the fields data preparation, feature engineering, model selection, selection of evaluation metrics, and hyperparameter optimization.
Automated machine learning can target various stages of the machine learning process. Essentially the targets can be grouped into the fields data preparation, feature engineering, model selection, selection of evaluation metrics, and hyperparameter optimization.
自动机器学习可以针对机器学习过程的不同阶段<ref name="AutoML2014ICML">{{Cite web|title=AutoML 2014 @ ICML|vauthors=Hutter F, Caruana R, Bardenet R, Bilenko M, Guyon I, Kegl B, and Larochelle H|work=AutoML 2014 Workshop @ ICML|date =|access-date=2018-03-28|url=http://icml2014.automl.org}}</ref>。从本质上看，这包括数据准备、特征工程、模型选择、评价指标的选择和超参数优化。
* Automated [[data preparation]] and ingestion (from raw data and miscellaneous formats)
* Automated [[data preparation]] and ingestion (from raw data and miscellaneous formats)
'''<font color="#ff8000">自动化数据准备 automated data preparation </font>'''和'''<font color="#ff8000">数据摄入 ingestion </font>'''（源于原始数据和混杂模式）
** Automated column type detection; e.g., boolean, discrete numerical, continuous numerical, or text
** Automated column type detection; e.g., boolean, discrete numerical, continuous numerical, or text
自动化数据类型检测，例如：布尔数据，离散数值，连续数值或者文本
自动化数据类型检测，例如：布尔数据，离散数值，连续数值或者文本
** Automated column intent detection; e.g., target/label, [[Stratified sampling|stratification]] field, numerical feature, categorical text feature, or free text feature
** Automated column intent detection; e.g., target/label, [[Stratified sampling|stratification]] field, numerical feature, categorical text feature, or free text feature
自动化数据意图检测，例如：目标/标签，分层抽样，数值特征，既定文本特征以及自由文本特征等
自动化数据意图检测，例如：目标/标签，'''<font color="#ff8000">分层抽样 stratified sampling </font>'''，'''<font color="#ff8000">数值特征 numerical feature </font>'''，'''<font color="#ff8000">既定文本特征 categorical text feature</font>'''以及'''<font color="#ff8000">自由文本特征 free tect feature </font>'''等
==[[用户:Yuling|Yuling]]（[[用户讨论:Yuling|讨论]]） categorical text feature, or free text feature 这两个应该是专业词汇，没有查到具体的翻译
==[[用户:Yuling|Yuling]]（[[用户讨论:Yuling|讨论]]） categorical text feature, or free text feature 这两个应该是专业词汇，没有查到具体的翻译
==[[用户:和光同尘|和光同尘]]（[[用户讨论:和光同尘|讨论]]）此处应该可以理解为检测意图包括对已明确类型结构的文本和自由文本两种不同类的文本模式各自特征的检测，因此可翻译为“既定文本特征以及自由文本特征”。
==[[用户:和光同尘|和光同尘]]（[[用户讨论:和光同尘|讨论]]）此处应该可以理解为检测意图包括对已明确类型结构的文本和自由文本两种不同类的文本模式各自特征的检测，因此可翻译为“既定文本特征以及自由文本特征”。
** Automated task detection; e.g., [[binary classification]], [[regression analysis|regression]], clustering, or [[learning to rank|ranking]]
** Automated task detection; e.g., [[binary classification]], [[regression analysis|regression]], clustering, or [[learning to rank|ranking]]
自动化任务检测，例如：'''<font color="#ff8000">二分类 binary classification </font>'''，'''<font color="#ff8000">回归分析 regression analysis</font>'''，'''<font color="#ff8000">聚类 clustering </font>'''，'''<font color="#ff8000">排序学习 learing to rank</font>'''
* Automated [[feature engineering]]
* Automated [[feature engineering]]
自动化特征工程
** [[Feature selection]]
** [[Feature selection]]
特征选择
'''<font color="#ff8000">特征选择 feature selection </font>'''
** [[Feature extraction]]
** [[Feature extraction]]
特征提取
'''<font color="#ff8000">特征提取 feature extraction </font>'''
** [[Meta learning (computer science)|Meta learning]] and [[transfer learning]]
** [[Meta learning (computer science)|Meta learning]] and [[transfer learning]]
'''<font color="#ff8000">元学习 meta learing </font>'''和'''<font color="#ff8000">体征转化 transfer learning </font>'''
** Detection and handling of skewed data and/or missing values
** Detection and handling of skewed data and/or missing values
'''<font color="#ff8000">偏斜数据 skewed data</font>'''和缺失值的检测和处理
* Automated [[model selection]]
* Automated [[model selection]]
自动化模型选择
自动化模型选择
* [[Hyperparameter (machine learning)#Optimization|Hyperparameter optimization]] of the learning algorithm and featurization
* [[Hyperparameter (machine learning)#Optimization|Hyperparameter optimization]] of the learning algorithm and featurization
'''<font color="#ff8000">特征工程 featurization</font>'''和'''<font color="#ff8000">学习算法 learning algorithm </font>'''中的超参数优化
* Automated pipeline selection under time, memory, and complexity constraints
* Automated pipeline selection under time, memory, and complexity constraints
在时间，内存和复杂性约束下的自动化流水线式选择
在时间，内存和复杂性约束下的自动化流水线式选择
* Automated selection of evaluation metrics / validation procedures
* Automated selection of evaluation metrics / validation procedures
自动化选择评估指标/验证程序
* Automated problem checking
* Automated problem checking
自动化问题检测
自动化问题检测
* [[Neural architecture search]]
* [[Neural architecture search]]
'''<font color="#ff8000">神经架构 neural architecture </font>'''搜索
* [[Hyperparameter optimization]]
* [[Hyperparameter optimization]]
超参数优化
超参数优化
* [[Model selection]]
* [[Model selection]]
模型选择
'''<font color="#ff8000"> 模型选择 model seletion</font>'''
* [[Neuroevolution]]
* [[Neuroevolution]]
神经进化
'''<font color="#ff8000">神经进化 neuroevolution </font>'''
* [[Self-tuning]]
* [[Self-tuning]]
自优化
'''<font color="#ff8000">自优化 self-tuning </font>'''