自动特征工程（Automatic Features Engineering）

Simply put

Automatic Feature Engineering, refers to the process of using machine learning algorithms and techniques to automatically discover, create, and select features from raw data. This is often done by applying statistical methods, pattern recognition, and domain knowledge to extract useful information from the data and transform it into features that can be used by machine learning models for prediction or classification tasks.

Automatic feature engineering aims to simplify and accelerate the feature engineering process, which is often considered to be the most challenging and time-consuming part of machine learning projects. By automating this process, data scientists and engineers can focus more on designing and optimizing their models, rather than spending countless hours manually creating features from scratch.

摘要

自动特征工程是一种通过特定的策略和算法来自动生成和选择特征的方法，以替代传统的人工设计和选择特征的过程。在这个过程中，系统会在预先定义的特征空间中进行搜索，以找到与问题领域相关的特征。此外，系统还会评估这些特征的效果，以便选择出最有效的特征。总的来说，自动特征工程的目标是提高模型的性能，减少人工干预的需求。

Key

Automatic Feature Type Judgment: This refers to a system’s ability to automatically determine the type of input features, such as numerical, categorical, date, and text types. This judgment can help with subsequent feature processing and modeling processes.
Automatic Question Type Judgment: This refers to a system’s ability to automatically determine the type of given question (e.g., classification problem, regression problem, text problem), so that appropriate algorithms and processing methods can be selected.
Automatic Categorical Feature Encoding: This refers to the automatic encoding of categorical features, converting them into numerical features for machine learning algorithms to process. Common methods include one-hot encoding and label encoding.
Automatic Label Encoding and Decoding: This refers to the automatic encoding of classification labels or output variables as numerical features for processing, and the ability to automatically decode back to the original label form in subsequent needs.
Automatic Feature Dimensionality Reduction: For data in high-dimensional feature spaces, automatic feature dimensionality reduction can reduce the dimensions of features through various methods, so as to better conduct model training and prediction.
Automatic Processing of Missing Values and New Features in Inference Process: During inference or prediction, there may be missing values or newly emerged features in the input data. The method of automatically handling missing values can fill in the existing data, while the method of automatically handling new features can map them to suitable feature representations.
Automatic Generation of Effective Features from Date-Type Features: For date-type features, effective features can be automatically extracted, such as year, month, quarter, day of the week, etc. These features can help models better understand and utilize time information.

关键点

自动特征类型判断：这是指一个系统能够自动判断输入的特征的类型，如是否为数值型、分类型、日期型、文本型等。这样的判断可以帮助后续的特征处理和建模过程。
自动问题类型判断：这是指一个系统能够自动判断给定的问题是属于什么类型（如分类问题、回归问题、文本问题等），以便选择合适的算法和处理方法。
类别特征自动编码：这是指将分类型的特征进行自动编码，将其转换为数值型特征以便机器学习算法可以处理。常见的方法包括独热编码和标签编码。
标签自动编解码：这是指对于分类型的标签或输出变量，将其自动编码为数值型特征进行处理，并且在后续需要时能够自动解码回原始的标签形式。
自动特征降维：对于高维特征空间中的数据，自动特征降维可以通过各种方法减少特征的维度，以便更好地进行模型训练和预测。
推理过程自动处理缺失值和新特征：在推理或预测过程中，输入的数据可能存在缺失值或新出现的特征。
自动处理缺失值的方法可以根据已有的数据进行填充，而自动处理新特征的方法可以将其映射到合适的特征表示。
日期型特征自动生成有效特征：对于日期型的特征，可以自动从中提取出有效的特征，如年份、月份、季度、周几等。这些特征可以帮助模型更好地理解和利用时间信息。

自动特征工程鲁棒性和自动特征工程高效性

自动特征工程鲁棒性：自动特征工程鲁棒性是指自动特征选择方法在面对不同的问题和数据集时，能够保持稳定且有效的性能。这意味着，无论输入数据的分布如何变化，或者问题的复杂性如何改变，自动特征选择方法都能够找到一组有效的特征来解决问题。这种稳定性使得自动特征选择方法在处理不同类型的数据和问题时具有更强的适应性。
自动特征工程高效性：自动特征工程高效性是指自动特征选择方法能够在较短的时间内完成特征选择和降维任务。这对于大规模数据和复杂模型来说尤为重要，因为它们通常需要大量的计算资源和时间来进行特征选择和降维。高效的自动特征选择方法可以在短时间内找到一组有效的特征，从而节省计算资源和时间，提高模型的训练效率。

Robustness and Efficiency of automatic feature engineering

Robustness of automatic feature engineering: The robustness of automatic feature engineering refers to the ability of automatic feature selection methods to maintain stable and effective performance when facing different problems and datasets. This means that no matter how the distribution of input data changes, or how the complexity of the problem changes, automatic feature selection methods can always find a set of effective features to solve the problem. This stability makes automatic feature selection methods more adaptable when dealing with different types of data and problems.
Efficiency of automatic feature engineering: The efficiency of automatic feature engineering refers to the ability of automatic feature selection methods to complete the task of feature selection and dimensionality reduction in a short time. This is especially important for large-scale data and complex models, as they usually require a lot of computational resources and time for feature selection and dimensionality reduction. Efficient automatic feature selection methods can quickly find a set of effective features, thus saving computational resources and time, and improving the training efficiency of the model.

自动机器学习中的特征工程

在自动学习中，自动特征工程扮演着至关重要的角色。它 是连接原始数据和机器学习模型的桥梁 ， 负责将原始数据转化为适合机器学习模型处理的特征表示 。在这个过程中，自动特征工程会根据预设的策略和算法， 自动生成和选择特征 ，以替代传统的人工设计和选择特征的过程。

自动特征工程的主要作用包括：

提高模型的性能 ：通过自动生成的特征，模型可以更好地捕捉到数据中的模式和信息，从而提高模型的预测性能。
减少人工干预的需求 ：传统的特征工程和选择过程往往需要大量的人工参与，而自动特征工程可以自动化这个过程，大大降低了对人工的需求。
提高模型的泛化能力 ：通过自动生成的特征，模型可以从更多的角度理解和表示数据，从而提高模型的泛化能力。
加速模型的开发和应用 ：自动特征工程可以大大提高模型的开发效率，使得模型可以快速地进行训练和应用。

Automatic feature engineering in AutoML

In automated learning, automatic features engineering plays a crucial role. It serves as a bridge between raw data and machine learning models, responsible for transforming raw data into feature representations suitable for processing by machine learning models. During this process, automatic features engineering generates and selects features automatically, replacing the traditional manual design and selection of features.

The primary functions of automatic features engineering include:

Improving model performance: By generating features automatically, the model can better capture patterns and information in the data, thereby improving the predictive performance of the model.
Reducing the need for human intervention: Traditional feature engineering and selection processes often require substantial human involvement, while automatic features engineering can automate this process, significantly reducing the need for human labor.
Enhancing the generalization ability of the model: By generating features automatically, the model can understand and represent data from multiple perspectives, thereby improving the generalization ability of the model.
Accelerating the development and application of the model: Automatic features engineering can greatly improve the development efficiency of the model, allowing it to be trained and applied quickly.