Explore the fundamental components of machine learning models by delving into the significance of input data. Understand the critical role it plays in shaping model outcomes and refining your understanding of the machine learning process. Gain insights into optimizing your data inputs for enhanced model performance.
Table of Contents
Question
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Select the answer that correctly completes the sentence.
In a machine learning model, the data that is used as inputs are called __________ .
A. features
B. functions
C. labels.
D. instances.
Answer
A. features
Explanation
The answer is A. features.
In a machine learning model, the data that is used as inputs are called features. Features are the characteristics or attributes of the data that can be used to make predictions or classifications. For example, if the data is about different types of flowers, some possible features are the color, shape, size, and number of petals. Features are usually represented as numerical values or categorical labels, such as 0 or 1, red or blue, etc. Features are also sometimes called independent variables or predictors, because they are used to explain or predict the output of the model .
The other options are incorrect for the following reasons:
- B. functions: Functions are mathematical expressions or algorithms that define how the model operates on the input data. Functions are not the data itself, but rather the rules or logic that the model uses to learn from the data and make predictions or classifications.
- C. labels: Labels are the data that is used as outputs or targets in a machine learning model. Labels are the values or categories that the model tries to predict or classify based on the input features. For example, if the data is about different types of flowers, a possible label is the species or name of the flower. Labels are also sometimes called dependent variables or responses, because they depend on or respond to the input features .
- D. instances: Instances are the individual data points or records in a machine learning model. Instances are composed of both features and labels, and they represent the observations or examples that the model learns from or makes predictions on. For example, if the data is about different types of flowers, an instance is a single flower with its features and label. Instances are also sometimes called samples or examples
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