Answers.Body: <p>If some of your input features are categorical, you could consider a TabTransformer model (see <a href="https://arxiv.org/abs/2012.06678" rel="nofollow noreferrer">paper</a> and <a href="https://www.amazon.science/blog/bringing-the-power-of-deep-learning-to-data-in-tables" rel="nofollow noreferrer">blog post</a>).
  There is an official Keras implementation and tutorial <a href="https://keras.io/examples/structured_data/tabtransformer/#experiment-2-tabtransformer" rel="nofollow noreferrer">here</a> and a third-party Pytorch version on <a href="https://github.com/lucidrains/tab-transformer-pytorch" rel="nofollow noreferrer">Github</a>. It may count as advanced enough because it uses Transformers, while still having a relatively simple neural network architecture.</p>
  <p>There are other examples of structured data classification on the Tensorflow/Keras websites that you can use for inspiration or comparison/baselines, e.g. <a href="https://www.tensorflow.org/decision_forests/tutorials/beginner_colab" rel="nofollow noreferrer">GBDTs</a>. Another option is to search past Kaggle competitions that are similar to your project, see what kind of advanced models scored highly and learn from their code.</p>
  <p>Personally I think anybody who can use Sklearn can use Keras, and I don't have a computer science background. Just look for a beginner-friendly tutorial that suits your needs, ideally one for the latest versions of TF/Keras from the official websites (they've changed quite a bit over the last few years).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-18T22:50:01.580Z
Answers.LastActivityDate: 2022-07-18T22:50:01.580Z
Answers.UserDisplayName: Martina K
Body: <p>Good morning.
  I am a student running a project using medical data, predicting if the patient will or won't get a disease. The data has about 50k cases and 70 features.
  I proposed to train 5 models- SVM, KNN, LR, RF and a neural network on this data, using cross-validation to optimize the hyperparameters and then report the best AUROC.
  However I've been told by my professor that using these models isn't 'advanced' enough and I need to do something at a higher level.
  First- I must admit I am not an expert at ML- i have run similar projects before successfully but nothing more advanced. I have also read a lot of literature on disease prediction using tabulate data and I am struggling to find options that are more 'advanced' and yet still feasible for myself- I have only been learning computer science for 9 months so my abilities are limited. However, I do have about 5 weeks to develop these models so I know I can learn lots.</p>
  <p>Please can anybody suggest where I can start? I am really worried about failing the project based on the reaction of my professor to my proposal.</p>
  <p>Thanks in advance</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-18T13:31:04.377Z
Comments.Text: what have you found so far that was not advanced enough or advanced enough but not feasible tacking into account your skills? especially the latter is quite difficult to figure out by your question as it is.
Comments.UserDisplayName: Edoardo Guerriero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-18T14:58:15.017Z
Comments.Text: @EdoardoGuerriero thanks for your reply. My suggestions were SVM, KNN, Logistic Regression and Random Forest using Sklearn, however she told me 'Anybody can use Sklearn' and it wasn't appropriate. But all i've ever done is use Sklearn, so anything more challenging than that is above my skill level!
Comments.UserDisplayName: helpwithkeras
Tags: <python><scikit-learn>
Title: 'Advancing' basic models
UserDisplayName: helpwithkeras
ThreadText: # 'Advancing' basic models
  
  (Asked by: username_0 on 2022-07-18)
  
  Good morning.
  I am a student running a project using medical data, predicting if the patient will or won't get a disease. The data has about 50k cases and 70 features.
  I proposed to train 5 models- SVM, KNN, LR, RF and a neural network on this data, using cross-validation to optimize the hyperparameters and then report the best AUROC.
  However I've been told by my professor that using these models isn't 'advanced' enough and I need to do something at a higher level.
  First- I must admit I am not an expert at ML- i have run similar projects before successfully but nothing more advanced. I have also read a lot of literature on disease prediction using tabulate data and I am struggling to find options that are more 'advanced' and yet still feasible for myself- I have only been learning computer science for 9 months so my abilities are limited. However, I do have about 5 weeks to develop these models so I know I can learn lots.
  
  Please can anybody suggest where I can start? I am really worried about failing the project based on the reaction of my professor to my proposal.
  
  Thanks in advance
  
  **Tags:** python, scikit-learn
  
  > **Comments:**
  > **username_1**: what have you found so far that was not advanced enough or advanced enough but not feasible tacking into account your skills? especially the latter is quite difficult to figure out by your question as it is.  
  > **username_0**: @username_1 thanks for your reply. My suggestions were SVM, KNN, Logistic Regression and Random Forest using Sklearn, however she told me 'Anybody can use Sklearn' and it wasn't appropriate. But all i've ever done is use Sklearn, so anything more challenging than that is above my skill level!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-07-18. Score: 0 | Accepted answer
  
  If some of your input features are categorical, you could consider a TabTransformer model (see [paper](https://arxiv.org/abs/2012.06678) and [blog post](https://www.amazon.science/blog/bringing-the-power-of-deep-learning-to-data-in-tables)).
  There is an official Keras implementation and tutorial [here](https://keras.io/examples/structured_data/tabtransformer/#experiment-2-tabtransformer) and a third-party Pytorch version on [Github](https://github.com/lucidrains/tab-transformer-pytorch). It may count as advanced enough because it uses Transformers, while still having a relatively simple neural network architecture.
  
  There are other examples of structured data classification on the Tensorflow/Keras websites that you can use for inspiration or comparison/baselines, e.g. [GBDTs](https://www.tensorflow.org/decision_forests/tutorials/beginner_colab). Another option is to search past Kaggle competitions that are similar to your project, see what kind of advanced models scored highly and learn from their code.
  
  Personally I think anybody who can use Sklearn can use Keras, and I don't have a computer science background. Just look for a beginner-friendly tutorial that suits your needs, ideally one for the latest versions of TF/Keras from the official websites (they've changed quite a bit over the last few years).
  
  
  ---
  
  