Train a feature extractor on a list of examples with a single feature:

从新的样例提取特征：

Extract features from multiple new examples:

Train a feature extractor on a list of examples with multiple features:

Extract features from multiple new examples:

Train a feature extractor on a mixed-type dataset:

从新的样例提取特征：

Train a feature extractor from a list of associations:

从新的样例提取特征：

Extract features from multiple new examples:

Train a feature extractor from data given as feature lists:

从 Tabular 提取特征：

从 Dataset 提取特征：

Train a feature extractor from a dataset that contains missing values:

Define a feature extractor that requires no training:

Apply it on some text:

Specify the feature extractor "SentenceVector" on a single textual feature:

Apply it on some text:

Train a feature extractor using the "StandardizedVector" method:

Extract features from a new example:

Since this feature extractor is invertible, the FeatureExtractorFunction property "OriginalData" can be used to perform the inverse extraction:

Train a feature extractor on text using the "TFIDF" method followed by the "DimensionReducedVector" method:

Extract features on the training set:

Train a feature extractor on texts and images using the text-only "TFIDF" method:

Features will only be extracted from the text part:

Specify the feature extraction on multiple features by position:

Use the feature extractor on new features:

A list of two items will be assumed to be a single input of two features:

Train a feature extractor with the "IndicatorVector" method on only the second nominal variable:

The first nominal variable is dropped:

Use the Identity extractor method to copy the first variable:

The first variable is copied:

A variable can be copied multiple times:

Specify the feature extraction on multiple features by key:

Use the feature extractor on new features:

Using the feature extractor on a list will assume the same ordering of features as originally specified:

Generate a feature extractor using a custom function:

Apply the extractor on the training set:

Chain the custom extractor with the "StandardizedVector" method:

Conform data prior to processing:

Reduce the dimensionality of the output:

Create a feature extractor for textual data using the "SentenceVector" extractor with no training:

Input type is inferred from the specified extractor. Use the feature extractor on some examples:

Create a feature extractor for examples with implicit textual and image features:

Features will be extracted from both parts:

Train a feature extractor on textual data:

Train a feature extractor with the "IndicatorVector" method on nominal variables:

训练特征提取程序计算文本的词频逆向文件频率向量：

训练集的词频逆向文件频率矩阵可以在 SparseArray 中计算：

可视化矩阵：

"TFIDF" 方法也可以用于标记的数据（名义袋）：

Train a feature extractor on a list of DateObject instances:

从新的 DateObject 中提取特征：

A string date can also be given:

Train a feature extractor on a list of Graph instances:

从新图中提取特征：

Train a feature extractor on a list of TimeSeries instances:

Train a feature extractor on Molecule data:

Train a feature extractor on a list of Audio instances:

Get Information from a trained FeatureExtractorFunction:

Find the available properties:

Get information about the input and output types:

训练特征提取程序，给每个特征一个名称：

使用关联格式从新的样例提取特征：

依然可以使用列表格式：

使用 FeatureNames 设置名称，并在 FeatureExtraction[examples,{spec₁ext₁,…}] 中引用它们：

Extract features on a new example using the names to specify the features:

在简单数据集上通过 "IndicatorVector" 训练特征提取程序：

第一个特征被解释为数值型. 由于 "IndicatorVector" 方法仅作用于名义特征，第一个特征不变：

使用 FeatureTypes 执行作为名义的第一个特征的诠释：

Now both features are encoded as indicator vectors:

Creating a feature extractor with no training infers the expected data type from the specific extractor:

Specifying the feature type will override the assumption:

Apply to named features:

建立一个狗图片的数据集：

从这个数据集训练提取程序函数：

生成关于数据集的提取特征的 NearestFunction：

使用 NearestFunction，构建一个函数，显示数据集的最相近图像：

将这个函数用于不在这个数据集的图像：

这个特征提取程序函数也可以用于删除过于相似的图像对：

加载 Alice in Wonderland 的文本：

将文本拆分为单句：

在这些单句上训练特征提取程序：

生成带有单句特征的 NearestFunction：

使用 NearestFunction，构建一个函数，显示 Alice in Wonderland 中最相近的单句：

将这个函数用于几个查询：

从 ExampleData 中加载 "MNIST" 数据集，并保留图像：

将图像转换为数值数据，并将数据集分离成训练集和测试集：

数据集的维度为 784：

使用 "MissingImputed" 方法创建特征提取程序：

用 Missing[] 替换测试集向量的某些值，并可视化：

使用 FeatureExtractorFunction[…] 估算缺失值：

可视化原始图像、带有缺失值的图像和估算后的图像：