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Attribute (view source)

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For instance, in a dataset of housing prices, attributes might include the number of bedrooms, square footage of the home, its neighborhood location and age. These details are then utilized to train a machine learning model that predicts the price tag on an unseen new home based on those attributes.
For instance, in a dataset of housing prices, attributes might include the number of bedrooms, square footage of the home, its neighborhood location and age. These details are then utilized to train a machine learning model that predicts the price tag on an unseen new home based on those attributes.
==Types of Attributes==
Machine learning consists of two primary types of attributes: numerical (or continuous) attributes and categorical (or discrete) attributes.
Numeric attributes refer to any number that falls within a specified range. Examples of numerical attributes include age, weight, height and temperature.
Categorical attributes refer to values or categories that have a fixed range. Examples include gender, color, nationality and education level.
Additionally, attributes can be binary in nature - taking one of two possible values such as true/false or yes/no.


==Types of Attributes==
==Types of Attributes==
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Categorical attributes, on the other hand, take on a limited set of possible values. For instance, neighborhood is such an attribute. Values can either be nominal (meaning no inherent order exists) or ordinal (meaning there is one inherent order present).
Categorical attributes, on the other hand, take on a limited set of possible values. For instance, neighborhood is such an attribute. Values can either be nominal (meaning no inherent order exists) or ordinal (meaning there is one inherent order present).
Additionally, attributes can be binary in nature - taking one of two possible values such as true/false or yes/no.


==Importance of Attributes==
==Importance of Attributes==
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Selecting the correct attributes is essential in building an accurate predictive model. In some cases, certain attributes may be more pertinent than others and it's essential to comprehend their relationship with your target variable in order to select those that are most pertinent.
Selecting the correct attributes is essential in building an accurate predictive model. In some cases, certain attributes may be more pertinent than others and it's essential to comprehend their relationship with your target variable in order to select those that are most pertinent.
==Feature Engineering==
Feature engineering is the process of selecting and manipulating attributes to produce new, more insightful features that can enhance a model's performance.
Feature engineering may involve altering numerical attributes by normalizing or scaling them within a specified range, or it could involve converting categorical attributes into numerical features through techniques such as one-hot encoding or ordinal encoding.
Feature engineering seeks to produce features that accurately capture the most pertinent and significant details about data while eliminating irrelevant details and duplication.


==Preprocessing Attributes==
==Preprocessing Attributes==
Before attributes can be utilized in a machine learning model, they typically need to go through preprocessing. This usually involves altering the data in various ways so that it is in an accessible format for the model to use.
Before attributes can be utilized in a machine learning model, they typically need to go through preprocessing. This usually involves altering the data in various ways so that it is in an accessible format for the model to use.


Categorical attributes must be encoded as numerical values, such as by converting the neighborhood attribute into a set of binary variables (known as one-hot encoding). Numerical attributes may need to be scaled so they have similar dimensions since some machine learning algorithms require similar input data.
Categorical attributes must be encoded as numerical values, such as by converting the neighborhood attribute into a set of binary variables (known as one-hot encoding). Numerical attributes may need to be normalized or scaled to a specific range so they have similar dimensions since some machine learning algorithms require similar input data.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
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