Answers.Body: <p>You are correct.  This is not machine learning.  Your current approach is a rule-based approach to binary classification.  However, you misunderstand what makes something machine learning.  It is <em>not</em> that you need an &quot;upgrade in features&quot;.  Instead, you need to use an algorithm that takes examples (many pairs of features and truth values) and &quot;learns&quot; the relationship between the features and truth values.  This is what we call training.  Then, when you give the model new features, the model will be able to predict the corresponding truth values.  This is what we call inference.  There is no &quot;hard-coding&quot; / no &quot;rules&quot; that you need to program with machine learning.</p>
  
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Answers.CreationDate: 2022-10-22T18:50:42.247Z
Answers.Text: Thanks, What kind of machine learning algorithms are possible to handle my use case? and will they all be "black box", or can some of them show me an actual formula that I might be able to manually modify myself? 
  
  Also, I think that some ML algorithms might be looked at as "upgrade". For example, Amazon product recommendation algorithm, could have been rule based, like if users that are similar to me in A,B,C, bought Z, recommend Z to me too. But adding ML algorithm might "upgrade" the user experience, so what kind of upgrade could that be?
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Answers.Text: None of the machine learning algorithms are truly "black boxes".  They all yield "formulas", even deep neural networks, and all can be manually modified.  However, in practice the formulas of deep neural networks can be complex and difficult to interpret by humans, which makes them effectively "black boxes".
Answers.UserDisplayName: Snehal Patel
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Answers.Text: I agree that machine learning algorithms may yield improved performance over rule based approaches and can be considered "upgrades."  (This is context dependent and won't be true in all cases.)  This usage, however, is different from your usage of "upgrade" in your post, where you talk about "upgrade of features".
Answers.UserDisplayName: Snehal Patel
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Answers.CreationDate: 2022-10-22T20:10:22.417Z
Answers.Text: Algorithms that may be useful include decision trees, random forests, support vector machines, logistic regression, and neural networks.  Decisions trees are often the simplest to understand (least like a "black box") and surprisingly can yield competitive results as more sophisticated models.  Which model is right for you depends on a lot of things like the type of features you have (categorical vs. numerical; high vs. low dimensional; etc.).  Ultimately, the best performant model must be empirically determined.
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Body: <p>Let's say I have an algorithm that tries to predict if a given item will be bought or not, within an X timeframe, based on it's price and other attributes.</p>
  <p>To Do this right now, I'm collecting historic data about similar items, like prices, and I assume that if a new given item is cheaper then let's say, the avg. price of similar items, I can predict it will be bought within X timeframe, and in that case I will output &quot;1&quot; instead of &quot;0&quot;.</p>
  <p>All of this is rule based - the rule is to look at the average price, if below, output &quot;1&quot;.</p>
  <p>So, This isn't considered as machine learning, right? What kind of features will &quot;upgrade&quot; this algorithm to be considered as &quot;AI&quot;?</p>
  
Tags: <machine-learning><prediction><algorithm>
Title: Purchase prediction - What minimum feature will "upgrade" A rule-based algorithm into an "AI" algorithm?
UserDisplayName: thenondeveloper
ThreadText: # Purchase prediction - What minimum feature will "upgrade" A rule-based algorithm into an "AI" algorithm?
  
  (Asked by: username_0 on 2022-10-21)
  
  Let's say I have an algorithm that tries to predict if a given item will be bought or not, within an X timeframe, based on it's price and other attributes.
  
  To Do this right now, I'm collecting historic data about similar items, like prices, and I assume that if a new given item is cheaper then let's say, the avg. price of similar items, I can predict it will be bought within X timeframe, and in that case I will output "1" instead of "0".
  
  All of this is rule based - the rule is to look at the average price, if below, output "1".
  
  So, This isn't considered as machine learning, right? What kind of features will "upgrade" this algorithm to be considered as "AI"?
  
  **Tags:** machine-learning, prediction, algorithm
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-10-22. Score: 2
  
  You are correct. This is not machine learning. Your current approach is a rule-based approach to binary classification. However, you misunderstand what makes something machine learning. It is *not* that you need an "upgrade in features". Instead, you need to use an algorithm that takes examples (many pairs of features and truth values) and "learns" the relationship between the features and truth values. This is what we call training. Then, when you give the model new features, the model will be able to predict the corresponding truth values. This is what we call inference. There is no "hard-coding" / no "rules" that you need to program with machine learning.
  
  > **Comments:**
  > **username_0**: Thanks, What kind of machine learning algorithms are possible to handle my use case? and will they all be "black box", or can some of them show me an actual formula that I might be able to manually modify myself?   Also, I think that some ML algorithms might be looked at as "upgrade". For example, Amazon product recommendation algorithm, could have been rule based, like if users that are similar to me in A,B,C, bought Z, recommend Z to me too. But adding ML algorithm might "upgrade" the user experience, so what kind of upgrade could that be?  
  > **username_1**: None of the machine learning algorithms are truly "black boxes".  They all yield "formulas", even deep neural networks, and all can be manually modified.  However, in practice the formulas of deep neural networks can be complex and difficult to interpret by humans, which makes them effectively "black boxes".  
  > **username_1**: I agree that machine learning algorithms may yield improved performance over rule based approaches and can be considered "upgrades."  (This is context dependent and won't be true in all cases.)  This usage, however, is different from your usage of "upgrade" in your post, where you talk about "upgrade of features".  
  > **username_1**: Algorithms that may be useful include decision trees, random forests, support vector machines, logistic regression, and neural networks.  Decisions trees are often the simplest to understand (least like a "black box") and surprisingly can yield competitive results as more sophisticated models.  Which model is right for you depends on a lot of things like the type of features you have (categorical vs. numerical; high vs. low dimensional; etc.).  Ultimately, the best performant model must be empirically determined.  
  
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