Home/ Questions/Q 21797
Next
In Process

Technomantic Latest Questions

Maya
  • 3
  • 3
Begginer
Asked: 2025-05-31T01:41:04+00:00 In:

Please tell me your approach for feature selection in your machine learning projects?

  • 3
  • 3

Selecting the right features is key to model success. Discuss your process for identifying and selecting impactful features.

aiml

You must login to add an answer.

Need An Account,

2 Answers

  1. Hassaan Arif
    Enlightened

    When it comes to feature selection, my approach is a bit like dating apps

    I swipe left on features that don’t add value and swipe right on those that actually improve the relationship (aka model performance).

    First, I start with the basics: get rid of features that are basically just noise or have zero variance no point dating someone who never changes, right?

    Then I check correlations if two features are basically twins, I keep one to avoid awkward love triangles in the model.

    Next, I use some automated tools like Recursive Feature Elimination or tree-based feature importance to let the data do the heavy lifting kind of like letting your friends give honest opinions.

    Finally, I test my “matches” with cross-validation to make sure they’re not just a good look on paper but actually perform well in the wild.

    In short, I treat feature selection like finding the perfect date: a bit of instinct, a dash of science, and a lot of trial and error!

  2. kalim
    Begginer

    In my machine learning projects, feature selection is a crucial step that I take seriously, as it can significantly impact the model’s performance and interpretability.
    My approach usually begins with a strong understanding of the domain and the data itself. I make it a point to explore the dataset thoroughly using visualizations, descriptive statistics, and correlation matrices.
    This helps me get an intuitive feel for which features might be relevant and which ones could be redundant or noisy.
    Once I have a general idea, I start with removing features that are obviously irrelevant for example, IDs or columns with a high percentage of missing values that don’t contribute meaningfully.
    I also look out for features with low variance, as they typically add little value to the model. After that, I perform correlation analysis to detect multicollinearity.
    If two features are highly correlated, I usually retain the one that has a stronger relationship with the target variable and drop the other.
    Next, I move to more systematic methods. I often use feature importance techniques based on tree-based algorithms like Random Forest or XGBoost. These models provide a good measure of which features contribute most to the predictive power. In some cases, I apply Recursive Feature Elimination (RFE) to iteratively eliminate the least important features and assess the impact on model accuracy.
    Additionally, I rely on domain knowledge wherever possible. There are times when a feature might not seem statistically important but has known practical relevance in such cases, I choose to keep it and observe its effect during model validation. Finally, I validate my selected feature set using cross-validation to ensure the model generalizes well and avoids overfitting. This blend of intuition, statistics, and algorithmic selection helps me craft efficient and effective models.