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In machine learning and predictive modeling, an "x variable" typically refers to the independent variables or features in a dataset that are used to predict or estimate the dependent variable or target variable, often denoted as "y."

In statistical terms, x variables are the predictors or inputs that are utilized by a model to make predictions or classifications. These variables contain the information or characteristics that the model learns from to understand relationships, patterns, and dependencies to predict the outcome (y variable).

For instance, in a simple linear regression equation where you're predicting house prices based on the size of the house, the x variable (independent variable) would be the size of the house, and the y variable (dependent variable) would be the house price. The model tries to learn the relationship between the house size (x) and the house price (y).

In more complex machine learning models, there might be multiple x variables or features that are used simultaneously to predict the target variable. These features could include numerical values, categorical values, text, images, or any other type of data that can be used as input to the model.

Selecting and engineering the right x variables or features is crucial in building accurate and effective machine learning models. Proper feature selection, transformation, normalization, and handling missing values are important steps in preparing x variables to ensure that the model can effectively learn and generalize from the data.

There are a lot of synonyms for "x variables" that are used in different contexts. You may hear the following from time to time:

- Independent Variable
- Features
- Attributes
- Predictors
- Explanatory Variables
- Inputs
- Regressors or Covariates (especially in regression analysis)