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第17行： − + 第25行：
第25行： − 自动化端到端应用机器学习的过程还提供了生成更简单的解决方案、更快地创建这些解决方案以及通常优于手工设计模型的模型的优势。+ + 第35行：
第36行： − 在一个典型的机器学习应用程序中，实践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方法，使数据集适合机器学习。按照这些预处理步骤，从业人员必须执行算法选择和超参数优化，以最大限度地提高他们的机器学习模型的预测性能。显然，所有这些步骤都带来了自己的挑战，累积到了开始机器学习的一个重大障碍。+ 第43行：
第44行： − 不足之处是 AutoML 工具的附加参数，这些参数可能需要一些专业知识来设置自己。虽然这些超参数是存在的，但是 AutoML 极大地简化了非专家机器学习的应用。+ + 第96行：
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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>'''是将机器学习应用于实际问题的过程自动化的过程。Automl 涵盖了从原始数据集到可部署的机器学习模型的整个管道。Automl 作为一种基于人工智能的解决方案，被提出来应用机器学习的日益增长的挑战。自动化的高度自动化允许非专家使用机器学习模型和技术，而不需要首先成为这个领域的专家。
'''<font color="#ff8000">自动机器学习 Automated machine learning，AutoML</font>'''是可以把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应对机器学习应用方面的日益增长的挑战。自动机器学习中的高度自动化允许非专家使用机器学习模型和技术并且不需要首先成为这个领域的专家。
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还提供了更多的优势：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优于人工设计的模型。
== Comparison to the standard machine learning approach ==
== Comparison to the standard machine learning approach ==
与通常的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，从业人员必须执行算法选择和超参数优化，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。
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。
==Targets of automation==
==Targets of automation==
自动机器学习的目标<br>
Automated machine learning can target various stages of the machine learning process.<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> 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.<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> Essentially the targets can be grouped into the fields data preparation, feature engineering, model selection, selection of evaluation metrics, and hyperparameter optimization.
* [[Neural architecture search]]
* [[Neural architecture search]]
神经架构搜索
* [[Hyperparameter optimization]]
* [[Hyperparameter optimization]]
超参数优化
* [[Model selection]]
* [[Model selection]]
模型选择
* [[Neuroevolution]]
* [[Neuroevolution]]
神经进化
* [[Self-tuning]]
* [[Self-tuning]]
自优化