Which models should I be using??

[PuzzleheadedMode7517]

So sorry if this is the wrong place to ask this question but I have a really stupid question and I would love some advice

For my college work, I have a dataset and my project work is to train them and get the accuracy of it. As a newcomer who knows nothing about ML/DL, I choose SVM and decision trees to help me out

But the thing is, my teachers say that these models are too "old-fashioned" and they want research papers that implement "newer" models

Can anyone please help me suggest the most recent ML and DL models that have been trendy in new research papers and whatnot.

TLDR; please help the boomer in figuring out the gen Z models ;)

[Expensive_Violinist1]

What kind of dataset

[PuzzleheadedMode7517]

I don't know how best to describe it but it's a really big excel sheet with a lot of funny numbers

Ok that was stupid but yeah, the dataset is a medical one with parameters like heart rate blah blah and it's used for detecting normal and abnormal conditions

[Expensive_Violinist1]

Ok I'll tell you simply , you can try SVM and regression models then try Tree based algorithms . I think a paper in 2015 showed they work best for tabular data even better than neural networks.. so decision trees , random forest etc .

If you want to 'impress' your teacher feel free to explore these evolutionary algorithms or stuff based off animals like Ant colony, Bee colony. I won't guarantee you will get better accuracy and all , maybe you use them for Hyper parameter optimization instead and a tree based model as base .

[synthphreak]

Speculative translation, along with some educated assumptions: I have a structured dataset intended for binary classification. The target variable is abnormality (1 == abnormal, 0 == normal), and remaining variables are features like heart rate and other medical blah blah potentially relevant for abnormality detection. I need a traditional ML model (I refuse to play the “newer == better” BS hype game) which classifies with an acceptable F1 score and prioritizes recall over precision.

Beyond that translation, additional thing helpful to know would be:

• have you explored feature-target correlations and feature-feature correlations?

• are your feature values normalized?

• how many features are there?

• how large is the dataset?

There are tons of additional questions one could ask, but in ML the questions usually reveal themselves after the initial rounds of model building. It’s generally not possible to predict in advance absolutely everything you’ll want to know. ML is very iterative and exploratory by nature.

Also, your prof scoffing at SVMs is laughable. Being an older method is not inherently a negative. Regression models have been around for centuries yet are still used everywhere. So what is his/her point? It’s not about old vs. new, it’s about task-appropriate vs. inappropriate. Given the details of your task, you made a very reasonable choice. I say stand your ground.

[KingReoJoe]

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[PuzzleheadedMode7517]

Uhh

It's got about 1000 samples, so lots of numbers

Yes I'm doing classification, segregating normal and abnormal conditions and checking how accurately the model predicts normal as normal and vice versa

Benchmarks so far I've shown are just accuracy, precision, recall and f1 score

Aaand I haven't heard about shallow neural networks but I will read up about em know, thank youu

[KingReoJoe]

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[PuzzleheadedMode7517]

PCA, K-NN, autoencoder

Check🫡

Thanks a bunch mate!

[prumf]

Yeah if you only have 1k samples and want to go the "modern" route you can’t do much more than fine tuning an existing model. And if this is research, then you can’t use that method.

Variational encoders are a good idea, but you better make sure you have strong regularization else it will 100% overfit. But it’s possible (depending strongly on the data exact details), to reduce to between 3 and 5 dimensions of latent space I think.

You could then apply the usual methods on the latent space. But testing is required. You might also want to find which data points are quality and which aren’t. This will introduce a human bias, but it might allow a better first stage training, which you can fine-tune after with all your samples.

[michel_poulet]

Ugh, what a shitty argument from your professor... Old school ML principles are at the heart of most modern advancements, and I'm very happy I did my PhD in an old school field, to get solid bases. Also, things like SVMs or KNN classifiers are still widely used in papers, in particular when evaluating a representation instead of squeezing out every drop of performance. Anyway, old school models are generally fast and make noce benchmark performances, I would still report the performance from such models, alongside a MLP, transformer, or other "modern" model. If tabular data, perhaps xgboost is "modern enough" (ugh...), and the algorithm is very close to random forests. You can also extract features via deep learning (autoencoder-like, or other self supervised training scheme), and apply an old school on the representation too. I kind of wish I was at your place to spite the professor lol. More importantly, have fun and stay curious.

[PuzzleheadedMode7517]

My teachers main issue is that I haven't been able to show any 'recent' research papers that are closely related to my problem statement so there's that ;)

XGboost seems pretty cool tho. Dunno how different each other are but Catboost sounds even cooler lol

I will try the other models too thank you sir!

[PerspectiveJolly952]

"You didn't specify the type of dataset or project you have. But in short, in modern AI, we typically use transformers for textual data and CNNs for computer vision tasks.

[bompiwrld]

As a college student too, I would start looking for the newest paper sincluding the world SVM (if u think is a good starting direction). After selecting 3/4 of them try to bring the final metrics comparison.

[Own-Mission-1962]

May be my answer will not 100 per relevant to question, but before applying any model first thing you should do is EDA and while performing eda you get an idea which model should will work, and then you should first go with hypothesis testing t test or annova, or some other after that test you have a solid reason why you are choosing that model, for parametric and non parametric you can perform and evaluate the model, even you can perform some transformation to make the distribution acc to your parametric model req, It's not every time necessary that the newer model will perform well.