In machine learning, attributes are the data objects that are utilized.
Fields, features, and variables in machine learning are all terms used to describe attributes.
- Attributes are the predictors that influence a particular result in predictive models. Attributes are the pieces of data that are evaluated for natural groupings or connections in descriptive models.
Attributes of the Model
The columns in the data set used to develop, test, or score a model are known as data attributes. Model attributes of machine learning are the data representations that the model uses internally.
Both the data and the model can have the same attributes. A column labeled SIZE, for example, with the values M, L, and X is an attribute used by an algorithm to generate a model.
A nested column (let’s call it SALE), on the other hand, does not relate to a model property because it contains sales numbers for a group of goods. The data attribute can be SALE, but each product and its related sales figure is a model attribute, as is each row in the nested column.
A disparity between data attributes and model attributes is also caused by transformations. A transformation, for instance, can perform a computation on two data attributes and save the result in a new attribute.
The new attribute is a model attribute that doesn’t have a data counterpart. Outlier treatment and normalization are examples of modifications that cause the model’s attribute to differ from the attribute in the case table.
This is a certain type of attribute and its historical values are stored in the target column of the training data. The historical values to which the predictions are compared are stored in the target column of the test data. The act of scoring generates a target forecast.
- A target is not used in clustering, feature extraction, association, or anomaly detection models. Targets cannot be nested columns or columns with unstructured data (such as BLOB, BFILE, or CLOB).
The model signature is a collection of data attributes used to construct a model. For scoring, some or all of the attributes in the signature must be present. On a best-effort basis, the model accommodates any missing columns. The model tries to convert the data type if there are columns with the same names but distinct data types. If there are any extra, unused columns, they are ignored.
- The model signature does not have to include all of the construction data columns. Certain columns may be ignored by the model due to algorithm-specific requirements. Transformations can be used to get rid of other columns. The signature only includes the data attributes that were actually utilized to generate the model.
The signature does not include the target or case ID fields.
The name of a model attribute is made up of two parts: a column name and a subcolumn name.
The data attribute’s name is represented by the column name component. It appears in the names of all model attributes. Nested attributes and text attributes both have a subcolumn name component.
Model details provide information on model attributes and how the algorithm handles them. Users should use the model views for the respective algorithm.
Before the algorithmic processing that builds the model, transformations are made to the attributes.
Model transparency is aided through reverse transformations. They give users a perspective of the data that the algorithm is working with internally but in a user-friendly style.
Unstructured texts and numerical
The attributes of a model can be unstructured text, numerical, or categorical. Oracle data types apply to columns in the case table (data attributes).
- Theoretically, numerical qualities can have an endless number of values. The values are ordered implicitly, and the discrepancies between them are ordered as well.
Values for categorical attributes identify a finite number of discrete categories or classes. The values do not have any implicit order. Some are binary, meaning they only have only two values. Other categoricals have more than two values.
It can be thought of as a data field that represents the attributes or features in machine learning of a data object. Customer Id, address, and so on are examples of attribute selections for a customer. A set of attributes used to describe a given object is referred to as an attribute vector or machine learning feature vector.
There are different types of attributes:
- Quantitative (Continuous, Numeric, and Discrete)
- Qualitative (Nominal, Binary, and Ordinal Ordinal).