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第17行：
第17行： − + 第25行：
第25行： − 在机器学习的应用中，将端到端的过程自动化可以提供更多的优势：生成更简单的解决方案、更快地创建这些解决方案以及在通常情况下设计出优于人工设计的模型。+ − 与通常的机器学习方法的比较<br>+ 第36行：
第36行： − 在一个典型的机器学习应用程序中，程序的使用者有一个由输入数据点组成的数据集来进行训练。原始数据本身的形式可能并不适用于所有算法。专家可能需要应用适当的数据预处理、特征工程、特征提取和特征选择这样的方法，使数据集适合机器学习。按照这些预处理步骤，程序的使用者必须执行算法的选择和超参数优化，以最大限度地提高他们的机器学习模型的预测性能。显然，所有这些步骤都为自身带来了挑战，当累积到了一定程度就成为机器学习的一个重大障碍。+ 第44行：
第44行： − 自动机器学习这一工具的不足之处就是需要附加参数，这些参数可能需要一些专业知识来设置。尽管有这些超参数存在，但是自动机器学习依旧极大地简化了非专家机器学习的应用。+ 第55行：
第55行： − 自动机器学习可以针对机器学习过程的不同阶段。从本质上讲，目标可以分为数据准备、特征工程、模型选择、评价指标的选择和超参数优化。+ − 自动化数据准备和数据摄入（从原始数据到混杂格式）+ − 自动化数据类型检测，例如：布尔变量，离散数值，连续数值或者文本+ − + + 第81行：
第82行： − 在时间，内存和复杂性约束下流程的自动选择+ 第93行：
第94行： − 用于自动化机器学习的用户界面及可视化+
无编辑摘要
Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring to become an expert in this field first.
Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring to become an expert in this field first.
'''<font color="#ff8000">自动机器学习 Automated machine learning，AutoML</font>'''是可以将机器学习应用于实际问题这一过程自动化的方法。自动机器学习涵盖了从原始数据集到可部署机器学习模型的整个流程。作为一种基于人工智能的解决方案，自动机器学习被提出来用于应对机器学习应用方面日益增长的挑战。自动机器学习中高度的自动化允许非专家使用机器学习的模型和技术并且不需要已经成为这个领域的专家。
'''<font color="#ff8000">自动机器学习 Automated machine learning，AutoML</font>'''是指实现机器学习自动应用于实际问题的过程。自动机器学习涵盖了从原始数据集到可部署机器学习模型的整个流程。作为一种基于人工智能的解决方案，自动机器学习被用于应对机器学习方面日益增长的挑战<ref name="autoweka1">{{cite conference|year=2013|title=Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms|url=https://dl.acm.org/citation.cfm?id=2487629|conference=KDD '13 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining|pages=847–855|vauthors=Thornton C, Hutter F, Hoos HH, Leyton-Brown K}}</ref><ref name="AutoML2014ICML"/>。自动机器学习中高度的自动化允许非专业人员在无需成为该领域专家的前提下使用机器学习的模型和技术。
Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.
Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.
在机器学习的应用中，将端到端的过程自动化可以产生更多优势：生成更简单的解决方案、更快地创建这些解决方案，并且经常能设计出优于人工设计的模型。
== Comparison to the standard machine learning approach ==
== Comparison to the standard machine learning approach ==
与常规机器学习方法的比较<br>
In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form such that all algorithms may be applicable to it out of the box. An expert may have to apply appropriate [[data pre-processing]], [[feature engineering]], [[feature extraction]], and [[feature selection]] methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform [[algorithm selection]] and [[hyperparameter optimization]] to maximize the predictive performance of their machine learning model. Clearly all of those steps induce their own challenges, accumulating to a significant hurdle to get started with machine learning.
In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form such that all algorithms may be applicable to it out of the box. An expert may have to apply appropriate [[data pre-processing]], [[feature engineering]], [[feature extraction]], and [[feature selection]] methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform [[algorithm selection]] and [[hyperparameter optimization]] to maximize the predictive performance of their machine learning model. Clearly all of those steps induce their own challenges, accumulating to a significant hurdle to get started with machine learning.
In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form such that all algorithms may be applicable to it out of the box. An expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their machine learning model. Clearly all of those steps induce their own challenges, accumulating to a significant hurdle to get started with machine learning.
In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form such that all algorithms may be applicable to it out of the box. An expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their machine learning model. Clearly all of those steps induce their own challenges, accumulating to a significant hurdle to get started with machine learning.
在一个典型的机器学习应用程序中，程序的使用者会使用一个由输入数据点组成的数据集去进行训练。原始数据本身的形式可能并不适用于所有算法。专家可能需要使用相应的'''<font color="#ff8000">数据预处理 data pre-processing </font>'''、'''<font color="#ff8000">特征工程 feature engineering</font>'''、'''<font color="#ff8000">特征提取 feature extraction</font>'''和'''<font color="#ff8000">特征选择方法 feature selectin methods</font>'''等方法，使数据集适合机器学习。按照这些预处理步骤，程序的使用者必须执行'''<font color="#ff8000">算法选择 algorithm </font>'''和'''<font color="#ff8000">超参数优化 hyperparameter optimization</font>'''，以最大限度地提升他们的机器学习模型的预测性能。显然，这些步骤都为它们自身带来了挑战。这些挑战一旦累积到一定程度，就会成为机器学习的重大障碍。
A downside are the additional parameters of AutoML tools, which may need some expertise to be set themselves. Although those hyperparameters exist, AutoML simplifies the application of machine learning for non-experts dramatically.
A downside are the additional parameters of AutoML tools, which may need some expertise to be set themselves. Although those hyperparameters exist, AutoML simplifies the application of machine learning for non-experts dramatically.
自动机器学习这一工具的不足之处就是对附加参数的依赖。这些参数可能需要一些专业知识才能得出。尽管有这些超参数存在，自动机器学习依旧极大地简化了非专业性机器学习的应用。
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。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)
自动化数据准备和数据摄入（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
** 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，文本类别特征，自由文本特征
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
==[[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”。
** 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：二分类，回归分析，聚类，排序学习
自动化任务检测，例如：二分类，回归分析，聚类，排序学习
特征工程和学习算法中的超参数优化
特征工程和学习算法中的超参数优化
* 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
* Automated selection of evaluation metrics / validation procedures
* Automated selection of evaluation metrics / validation procedures
自动选择评估指标/验证程序
自动选择评估指标/验证程序
自动分析获得的结果
自动分析获得的结果
* User interfaces and visualizations for automated machine learning
* User interfaces and visualizations for automated machine learning
用于自动化机器学习的用户界面及可视性