The variables are the columns in the data table. Variables might represent physical measurements (temperature, velocity,…), personal characteristics (gender, age,…), marketing dimensions (recency, frequency, monetary, etc.), etc.
The data set contains information for creating our model. It is a data collection structured as a table in rows and columns.
https://www.neuraldesigner.com/learning/tutorials/data-set/#InputVariables
So I think it's dataset
Agreed. In traditional ML, the variables are what columns you would like to use as inputs within the model architecture. The dataset is then the collection of distinct rows that are used to train the model.
Saying that, I think the answer is still variables because it specifies the inputs of the 'model' as opposed to the data that is used to train it.
You see, it's even in the link itself: these are input *variables*. Beside input dataset may contain labels (target variables) and unused variable. Thus, correct answer is C.
n a machine learning model, the data that is used as inputs are called "variables". Variables are the features or attributes of the data that the model uses to make predictions or decisions. They can include numerical values, categorical values, or a combination of both.
A dataset is a collection of data that may include multiple variables, but it's not the term used specifically to refer to the inputs of a machine learning model.
Labels are the target values or outcomes that the machine learning model is trying to predict. They're used during the training phase to teach the model to associate certain inputs (the variables) with certain outputs (the labels), but they're not the inputs themselves.
Therefore, the correct term to complete the sentence "In a machine learning model, the data that is used as inputs are called ________" is "variables". C is thus the answer.
C is the answer.
https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine-learning-designer/2-regression-scenarios
Regression is a form of machine learning used to understand the relationships between variables to predict a desired outcome. Regression predicts a numeric label or outcome based on variables, or features. For example, an automobile sales company might use the characteristics of a car (such as engine size, number of seats, mileage, and so on) to predict its likely selling price. In this case, the characteristics of the car are the features, and the selling price is the label.
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