Answers.Body: <p>Deep learning models are generally not the first choice for tabular (financial) data due to their inefficiency in capturing feature interactions compared to traditional methods like decision trees, random forests or gradient boosting with shrinkage. Given that your dataset is 100k rows, deep learning may be overkill which is usually trained with millions of samples in the form of very high-dimensional images, texts, etc.</p>
  
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Answers.Text: Thank you for the answer. To give more clarification, the data set is not about financial fraud but concerns users for a web app— I don’t think this changes anything though. Do you think the two step approach of using supervised and unsupervised ml is overkill?
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Answers.Text: Can you provide some research that supports the claim that neural networks are inefficient in capturing feature interactions (for tabular data)?
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Answers.Text: Also, neural networks don't necessarily have millions of layers. You can crease simple neural networks, although the definition of what a deep neural network is is a bit fuzzy. It's unclear to me if the user wants just to use a neural network, even if it's not that deep.
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Answers.Text: @nbro. My current choice is to use something like Catboost and supplement it with DBScan or Isolation forest to catch newer forms of fraud. My understanding is that neural networks are black boxes and hard to extract information from. My project is for a class, so I would like to be able to explain what features  the model's predictions are based on
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Answers.Text: @nbro my above answer advises DL models are generally not the first choice for tabular data, and its inefficiency in capturing feature interactions is in the exact sense of OP's comment here that neural networks are black boxes and lack explanability especially for Fraud or other regulatory critical apps. Regulators wont' get impressed by how complex and deep your model is, they want explanation of model's decision making to check if it's aligned with their codes. Also my answer never claims DL models *have millions of layers*, just needs millions of samples to train usually like LLMs/Dale-E.
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Answers.Text: @Burger your approach may not be overkill. The anomaly detection is adding value as there might be situations where new patterns of user behavior emerge over time. Using unsupervised models can help catch these novel cases, particularly in dynamic environments like web apps where user behavior can evolve quickly. With 100k rows, you likely have enough historical data to train a robust supervised model. However, depending on the feature set, this might still leave gaps in the model’s ability to detect entirely new behaviors or subtle shifts, and anomaly detection can address such edge cases.
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Answers.Text: CatBoost can provide feature importance scores, which show how much each feature contributes to the overall model's decision-making process and can be crucial in fraud detection. While global feature importance provides an overall understanding of which features matter most, SHAP values or LIME can provide local interpretability, explaining individual predictions. This is particularly useful for showing why a specific user is flagged as fraudulent. Fraud features are unlikely as complex as multi-level hierarchical face, so DL is not suitable in addition to interpretability. Hope now clarifies.
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Body: <p>My dataset is around 100k rows and is tabular data.</p>
  <p>My question is this: Is there a case to use deep learning here?</p>
  <p>My current approach is the following: 1) training a supervised on historical data and then 2) running an unsupervised machine learning model like clustering or anomaly detection.</p>
  <p>Then, for any new users, they would receive a predicted score from the supervised model and would get flagged if they are an anomaly. This way, I could balance historical patterns but also account for new fraud patterns. The supervised model is a regression model and would have target values from 0 to 1.</p>
  
Tags: <machine-learning><deep-learning><regression>
Title: When to use deep learning for bot and fraud detection and when not to use it?
UserDisplayName: Burger
ThreadText: # When to use deep learning for bot and fraud detection and when not to use it?
  
  (Asked by: username_0 on 2024-11-19)
  
  My dataset is around 100k rows and is tabular data.
  
  My question is this: Is there a case to use deep learning here?
  
  My current approach is the following: 1) training a supervised on historical data and then 2) running an unsupervised machine learning model like clustering or anomaly detection.
  
  Then, for any new users, they would receive a predicted score from the supervised model and would get flagged if they are an anomaly. This way, I could balance historical patterns but also account for new fraud patterns. The supervised model is a regression model and would have target values from 0 to 1.
  
  **Tags:** machine-learning, deep-learning, regression
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-11-20. Score: -1 | Accepted answer
  
  Deep learning models are generally not the first choice for tabular (financial) data due to their inefficiency in capturing feature interactions compared to traditional methods like decision trees, random forests or gradient boosting with shrinkage. Given that your dataset is 100k rows, deep learning may be overkill which is usually trained with millions of samples in the form of very high-dimensional images, texts, etc.
  
  > **Comments:**
  > **username_0**: Thank you for the answer. To give more clarification, the data set is not about financial fraud but concerns users for a web app— I don’t think this changes anything though. Do you think the two step approach of using supervised and unsupervised ml is overkill?  
  > **username_2**: Can you provide some research that supports the claim that neural networks are inefficient in capturing feature interactions (for tabular data)?  
  > **username_2**: Also, neural networks don't necessarily have millions of layers. You can crease simple neural networks, although the definition of what a deep neural network is is a bit fuzzy. It's unclear to me if the user wants just to use a neural network, even if it's not that deep.  
  > **username_0**: @nbro. My current choice is to use something like Catboost and supplement it with DBScan or Isolation forest to catch newer forms of fraud. My understanding is that neural networks are black boxes and hard to extract information from. My project is for a class, so I would like to be able to explain what features  the model's predictions are based on  
  > **username_1**: @nbro my above answer advises DL models are generally not the first choice for tabular data, and its inefficiency in capturing feature interactions is in the exact sense of OP's comment here that neural networks are black boxes and lack explanability especially for Fraud or other regulatory critical apps. Regulators wont' get impressed by how complex and deep your model is, they want explanation of model's decision making to check if it's aligned with their codes. Also my answer never claims DL models *have millions of layers*, just needs millions of samples to train usually like LLMs/Dale-E.  
  > **username_1**: @Burger your approach may not be overkill. The anomaly detection is adding value as there might be situations where new patterns of user behavior emerge over time. Using unsupervised models can help catch these novel cases, particularly in dynamic environments like web apps where user behavior can evolve quickly. With 100k rows, you likely have enough historical data to train a robust supervised model. However, depending on the feature set, this might still leave gaps in the model’s ability to detect entirely new behaviors or subtle shifts, and anomaly detection can address such edge cases.  
  > **username_1**: CatBoost can provide feature importance scores, which show how much each feature contributes to the overall model's decision-making process and can be crucial in fraud detection. While global feature importance provides an overall understanding of which features matter most, SHAP values or LIME can provide local interpretability, explaining individual predictions. This is particularly useful for showing why a specific user is flagged as fraudulent. Fraud features are unlikely as complex as multi-level hierarchical face, so DL is not suitable in addition to interpretability. Hope now clarifies.  
  
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