r/MLQuestions

Hi everyone, I'm currently a bachelor of technology student at a top tier indian institution.

I just see seniors/people talking on how to build 2-3 solid and impactful projects for resume, and they usually say, first select a particular domain/niche of CS by exploring everything and see your interests. And then, after you've found your interests, dive deep into it and make 2-3 solid projects which are impactful and solve some real-world problem too, with user engagement. This works in current job market as well.

My question is how do you dive deep once you've selected a particular niche, say AI/ML ?

I understand the math behind Ridge (L2) and Lasso (L1) regression — cost functions, gradients, and how regularization penalizes coefficients during optimization.

What I’m struggling with is the intuition and geometry behind why they behave differently.

Specifically:

- Why does Ridge shrink coefficients smoothly but almost never make them exactly zero?

- Why does Lasso actually push some coefficients exactly to zero (feature selection)?

I’ve seen explanations involving constraint shapes (circle vs diamond), but I don’t understand them.Thats the problem

From an optimization/geometric perspective:

- What exactly causes L1 to “snap” coefficients to zero?

- Why doesn’t L2 do this, even with large regularization?

I understand gradient descent updates, but I feel like I’m missing how the geometry of the constraint interacts with the loss surface during optimization.

Any intuitive explanation (especially visual or geometric) would help or any resource which helped you out with this would be helpful.

Hey all,

I recently built an end-to-end fraud detection project using a large banking dataset:

• Trained an XGBoost model
• Used Databricks for processing
• Tracked experiments and deployment with MLflow

The pipeline worked well end-to-end, but I’m realizing something during interview prep:

A lot of ML Engineer interviews (even for new grads) expect discussion around:

• What can go wrong in production
• How you debug issues
• How systems behave at scale

To be honest, my project ran pretty smoothly, so I didn’t encounter real production failures firsthand.

I’m trying to bridge that gap and would really appreciate insights on:

1. What are common failure points in real ML production systems? (data issues, model issues, infra issues, etc.)
2. How do experienced engineers debug when something breaks?
3. How can I talk about my project in a “production-aware” way ?
4. If you were me, what kind of “challenges” or behavioral stories would you highlight from a project like this?
5. Any suggestions to simulate real-world issues and learn from them?

Goal is to move beyond just “I trained and deployed a model” → and actually think like someone owning a production system.

Would love to hear real experiences, war stories, or even things you wish you knew earlier.

Thanks!

Hello! I did multinomial logistic regression to predict risk categories: Low, Medium and High. The model's performance was quite poor. The balanced accuracy came in at 49.28% with F1 scores of 0.049 and 0.013 for Medium and High risk respectively.

I think this is due to two reasons: the data is not linearly separable (Multinomial Logistic Regression assumes a linear log-odds boundary, which may not hold here), and the class imbalance is pretty bad, particularly for High risk, which had only 17 training observations. I did class weights but I don't think that helped enough.

I included a PCA plot (PC1 and PC2) to visually support the separability argument, but idk if the PCA plot is a valid support. Bc it’s not against the log-odds but idk yk. What I have in my report right now is:

As shown in Figure 1 above, all three risk classes overlap and have no discernible boundaries. This suggests that the classes do not occupy distinct regions in the feature space, which makes it difficult for any linear model to separate them reliably.

And I am just wondering if that's valid to say. Also this is in R!

https://preview.redd.it/yf246cimn6tg1.png?width=407&format=png&auto=webp&s=34ef165d5dfc93597152222c594fddc9c9a8a383

My dataset is relatively small (233 samples) and highly nonlinear (concrete strength). I have tried both 5-fold and 10-fold cross-validation, along with an 80:20 train–test split. While the test R² appears reasonable, the cross-validation R² is quite low. What can I do to improve this?

Working on my research paper on vehicle classification and image detection and have to train the model on YOLOv26m , my system(rtx3060 ,i7, 6 Gb graphics card and 16Gb RAM) is just not built for it , the dataset itself touches around 50-60 gb .
I'm running 150 epochs on it and one epoch is taking around 30ish min. on image size which i degraded from 1280px to 600px cause of the system restrains .

Is there any way to train it faster or anyone experiences in this could contribute a little help to it please.

I'm an absolute beginner, trying to figure things out.

i have been tasked with a small analytics project by one of my managers, it should demonstrate the use of Analytics and AI and to suggest where AI could be incorporated into business more generally.

I work for BT Group so I'm mainly dealing with a data set in the telecommunications industry and I'm trying to build a churn prediction model. got a small data set of about 3000 entries with 13 features

mainly using python with Google collab

ive thought to do the basic steps like

-data understanding & Exploratory data analysis (some visualisation)

-data preprocessing

-train test split

-ML pipeline development

-model training

-hyperparameter tuning

-model evaluation

Could you guys suggest a better way of doing things and also, how do I include GenAI into this problem

Need help with choosing a field to do research on asap 😭 So I’m joining an AI lab at my uni and it involved application of AI, machine learning and deep learning on many fields: computer vision, fraud detection, LLM, medical…. And upon application, I need to choose a specific field to follow. Initally, my top choice was fraud detection but ppl in the lab said that it was really hard and a lot of pure math involved. That really scared me so I’m thinking of s

witching to maybe AI in medical field or LLM. Please give your opinion and help me choose! Thank you!

I’ve been reading about TabPFN and the claims around strong performance on tabular data with minimal tuning. On paper it looks impressive, but I’m curious about real-world experience.

For people who’ve actually tried it:

• Where did it work well?

• Where did it fall short?

• How does it compare to e.g. XGBoost / LightGBM in practice?

• Any gotchas (data size limits, stability, interpretability, etc.)?

Not looking for hype but rather honest experiences, good or bad.

I want to build an app that uses one of them to dive into legal statutes and stuff . I haven't began to learn it yet, just asking