Answers.Body: <p>An <strong>input</strong> involves everything as a data. For example in image classification, all images are input to model while <strong>features</strong> are the kind of specific properties of that input images based on model will decide the results.e.g in dog and cat dataset all images of dogs and cats is a input to model and properties such as <strong>ears, nose, other facial differences</strong> are the features of that input data.</p>
  
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Answers.Body: <p>An <strong>input</strong> usually refers to an <em>example</em> (sometimes also known as <em>sample</em>, <em>observation</em> or <em>data point</em>) <span class="math-container">$x$</span> from a dataset that you pass to the model. For example, in supervised learning, you have a labelled dataset <span class="math-container">$D = \{(x_i, y_i)\}_{i=1}^N$</span>, where <span class="math-container">$x_i$</span> is the <span class="math-container">$i$</span>th input and <span class="math-container">$y_i$</span> the corresponding label (aka target or output).</p>
  <p>This is similar to the terminology used for <a href="https://www.mathsisfun.com/sets/function.html" rel="nofollow noreferrer">functions</a>. For example, if you have the function <span class="math-container">$f: \mathcal{X} \rightarrow \mathcal{Y}$</span>, then <span class="math-container">$x \in \mathcal{X}$</span> is the input and <span class="math-container">$f(x) = y \in \mathcal{Y}$</span> is the output of the function for that input <span class="math-container">$x$</span>. In fact, models (like neural networks or linear regression models) are functions.</p>
  <p>Examples</p>
  <ul>
  <li>In image classification, an image can be an input</li>
  <li>In machine translation, an input can be a sentence or a word (depending on the model)</li>
  <li>In reinforcement learning, an input can be a state</li>
  </ul>
  <p>A <strong>feature</strong> is an <em>attribute</em> associated with an input or sample. For example, a feature of an image could be a pixel. The feature of a state could be the Euclidean distance to the goal state. An input can be composed of multiple features.</p>
  <p>It's possible that people also refer to features as inputs (in fact, if you pass e.g. an image to a model, you're also passing the pixels, the features, which are thus also inputs to the model). There are also other terms used to refer to these. For example, in statistics, people may refer to features as independent variables (or regressors), and maybe a sample refers to a dataset rather than a single observation. So, you should always take into account the context when reading these terms.</p>
  <p>For more info, you could read <a href="https://en.wikipedia.org/wiki/Dependent_and_independent_variables" rel="nofollow noreferrer">this</a> and <a href="https://en.wikipedia.org/wiki/Unit_of_observation" rel="nofollow noreferrer">this</a> Wikipedia articles.</p>
  
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Answers.Text: I read [this article](https://www.guru99.com/machine-learning-tutorial.html) and it says  `feature vector as a subset of data that is used to tackle a problem` So is feature a subset of input the ML model finds more useful to deal with?
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Answers.Text: @user0193 I think it's more accurate to think of a feature as an attribute of the input rather than a subset because the input might not be a set. A feature vector is a vector of features. Sometimes, in ML, you convert your original input to a transformed input, which contains more "useful" features. For example, if you're doing image classification, you could pass an image of pixels ("raw input") to the model. But what if you first transformed this image into another input (a vector - the feature vector), where, e.g., element $i$ represents whether there's a human face or not in the image?
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Answers.Text: With the transformed input (the feature vector), the model now might perform better because the new features (presence of a human face or not) are more informative than the original ones (pixels). You could also think of a raw image (with pixels) as a feature vector, but I don't know if this is common. In summary, I think it's better to think of features as attributes of the inputs. For example, my laptop is gray. Being gray is an attribute (aka feature). The laptop is the input and the color is a feature (attribute) of the input.
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Answers.Text: So essentially in convolution neural network from the second layer till the second-last layer subsequently extracts features across several layers, i.e. as per your moments "transforming input" and final layer looks into this transformed layer and making final decision/output in the final layer. Right?
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Answers.Text: @user0193 Yes, you think of a CNN, but also other neural networks, as transforming the input and as (automatic) feature extractors (based on the data, training objective and learning algorithm).
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Body: <p>I have seen many places that features and inputs have been used interchangeably when talking about machine learning especially deep neural networks. I want to know if they are indeed the same thing or there is a difference between between the two.</p>
  
Tags: <machine-learning><comparison><terminology><features>
Title: What is the difference between features and inputs in machine learning?
UserDisplayName: user0193
ThreadText: # What is the difference between features and inputs in machine learning?
  
  (Asked by: username_0 on 2022-05-13)
  
  I have seen many places that features and inputs have been used interchangeably when talking about machine learning especially deep neural networks. I want to know if they are indeed the same thing or there is a difference between between the two.
  
  **Tags:** machine-learning, comparison, terminology, features
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2022-05-13. Score: 5 | Accepted answer
  
  An **input** usually refers to an *example* (sometimes also known as *sample*, *observation* or *data point*) $x$ from a dataset that you pass to the model. For example, in supervised learning, you have a labelled dataset $D = \{(x\_i, y\_i)\}\_{i=1}^N$, where $x\_i$ is the $i$th input and $y\_i$ the corresponding label (aka target or output).
  
  This is similar to the terminology used for [functions](https://www.mathsisfun.com/sets/function.html). For example, if you have the function $f: \mathcal{X} \rightarrow \mathcal{Y}$, then $x \in \mathcal{X}$ is the input and $f(x) = y \in \mathcal{Y}$ is the output of the function for that input $x$. In fact, models (like neural networks or linear regression models) are functions.
  
  Examples
  
  * In image classification, an image can be an input
  * In machine translation, an input can be a sentence or a word (depending on the model)
  * In reinforcement learning, an input can be a state
  
  A **feature** is an *attribute* associated with an input or sample. For example, a feature of an image could be a pixel. The feature of a state could be the Euclidean distance to the goal state. An input can be composed of multiple features.
  
  It's possible that people also refer to features as inputs (in fact, if you pass e.g. an image to a model, you're also passing the pixels, the features, which are thus also inputs to the model). There are also other terms used to refer to these. For example, in statistics, people may refer to features as independent variables (or regressors), and maybe a sample refers to a dataset rather than a single observation. So, you should always take into account the context when reading these terms.
  
  For more info, you could read [this](https://en.wikipedia.org/wiki/Dependent_and_independent_variables) and [this](https://en.wikipedia.org/wiki/Unit_of_observation) Wikipedia articles.
  
  > **Comments:**
  > **username_0**: I read [this article](https://www.guru99.com/machine-learning-tutorial.html) and it says  `feature vector as a subset of data that is used to tackle a problem` So is feature a subset of input the ML model finds more useful to deal with?  
  > **username_2**: @username_0 I think it's more accurate to think of a feature as an attribute of the input rather than a subset because the input might not be a set. A feature vector is a vector of features. Sometimes, in ML, you convert your original input to a transformed input, which contains more "useful" features. For example, if you're doing image classification, you could pass an image of pixels ("raw input") to the model. But what if you first transformed this image into another input (a vector - the feature vector), where, e.g., element $i$ represents whether there's a human face or not in the image?  
  > **username_2**: With the transformed input (the feature vector), the model now might perform better because the new features (presence of a human face or not) are more informative than the original ones (pixels). You could also think of a raw image (with pixels) as a feature vector, but I don't know if this is common. In summary, I think it's better to think of features as attributes of the inputs. For example, my laptop is gray. Being gray is an attribute (aka feature). The laptop is the input and the color is a feature (attribute) of the input.  
  > **username_0**: So essentially in convolution neural network from the second layer till the second-last layer subsequently extracts features across several layers, i.e. as per your moments "transforming input" and final layer looks into this transformed layer and making final decision/output in the final layer. Right?  
  > **username_2**: @username_0 Yes, you think of a CNN, but also other neural networks, as transforming the input and as (automatic) feature extractors (based on the data, training objective and learning algorithm).  
  
  ---
  
  ### Answer by username_1 on 2023-03-25. Score: 0
  
  An **input** involves everything as a data. For example in image classification, all images are input to model while **features** are the kind of specific properties of that input images based on model will decide the results.e.g in dog and cat dataset all images of dogs and cats is a input to model and properties such as **ears, nose, other facial differences** are the features of that input data.
  
  
  ---
  
  