How to set the range for hyperparameter search space for Support Vector Ordinal Regression

I’m using Support Vector Ordinal Regression for a project and tuning:

  • Regularization strength
  • Formulation (EXC/IMC)
  • Kernel type (linear/RBF)

How do you usually determine the search space for these hyperparameters?

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u/Routine-Ticket-5208 — 4 days ago
▲ 3 r/MLQuestions+1 crossposts

Hyperparameter search space for Gradient Boosting Model

I’m using Gradient Boosting for a project and tuning:

  • Number of leaves
  • Minimum data in each leaf
  • Learning rate
  • Feature fraction

What’s a good way to decide on the search space for these hyperparameters?

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u/Routine-Ticket-5208 — 4 days ago

Need help with Random Forest Hyperparameter Search Space

I’m using Random Forest for a project and tuning:

  • Number of trees
  • Maximum tree depth
  • Number of features considered at each split
  • Minimum samples required to split a node

How do you normally determine the search space for these parameters?

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u/Routine-Ticket-5208 — 4 days ago
▲ 3 r/MLQuestions+1 crossposts

Help with Ordinal Logistic Regression Hyperparameter

I’m using Ordinal Logistic Regression for a project and doing hyperparameter search for:

  • Regularization strength
  • L1/L2 regularization

How do you usually decide on the search space for these hyperparameters?

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u/Routine-Ticket-5208 — 4 days ago
▲ 7 r/MLQuestions+1 crossposts

Mixing normalization methods (min-max + z-score) for different features

Working on a project where I extract a bunch of numeric features (some bounded 0-1, some counts with outliers, some skewed rates, etc.) and feed them into classical ML models.

A few questions I can't find a clean answer to:

  1. Is it normal to use different normalization methods for different features in the same pipeline? E.g., min-max for one feature, z-score for another, robust scaling for a third — based on each feature's own distribution, rather than applying one method to everything?
  2. If I mix min-max and z-score, don't they end up on different scales? Min-max gives you a clean 0–1 range. Z-score doesn't really have a hard bound — most values land somewhere around -3 to +3, but it's not guaranteed. So if I use both in the same feature set, I end up with some features on 0–1 and others on roughly -3 to +3. Doesn't that defeat the whole point of normalizing? Does that mismatch actually hurt model performance, or does it depend on the model?
  3. Is it common to do a two-step normalization — like z-score (or log transform) first to fix skew/outliers, then min-max on top of that to bring everything back to a consistent 0–1 range? Or is that overkill / bad practice?

Thank you

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u/Routine-Ticket-5208 — 12 days ago

Any Good French Courses or Language Exchange in Cambodia?

Hi everyone,

I’m looking for an affordable French class or short program in Cambodia. I’m also open to language exchange meetups or conversation groups.

I speak Khmer and English fluently, and I also speak Korean at a business level.

Any recommendations would be appreciated. Thank you!

reddit.com
u/Routine-Ticket-5208 — 3 months ago