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Computer Vision (1)
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Feature Engineering

Q. What are some of the most common feature engineering techniques?
Q. What is the difference between Feature Engineering and Feature Selection?
Q. What is Feature Scaling? Explain the different feature scaling techniques
Q. What is the purpose of feature selection, and what are some common approaches?
Q. What is Feature Standardization (or Z-Score Normalization), and why is it needed?
Q. How are categorical features or qualitative predictors represented in a machine learning model?
Q. What does Centering and Scaling mean? What is the individual effect of each of those?
Q. What are different ways to impute missing values for a feature?
Q. What are the different categories of missing data?
Q. What is Max Absolute Scaler? Compare it with MinMax Normalization? Why scaling to [-1, 1] might be better than [0, 1] scaling?
Q. What is MinMax Normalization? Compare MinMax Normalization with Z-Score Standardization
Q. Among the common machine learning algorithms, which require feature scaling, and which do not?
Q. How to perform Standardization in case of outliers?
Q. What is the problem with storing sparse two-dimensional training data (feature_vector x n_sample)? What is a space optimal way to store such a matrix?
Q. What is Data Sparsity?
Q. What is Data Leakage?
Q. What is Normalization?
Q. Discuss Discretization in the context of feature engineering
Q. Discuss Timestamp Date Extraction in the context of feature engineering
Q. Discuss text feature extraction in the context of feature engineering
Q. Discuss Ordinal encoding in the context of feature engineering
Q. Discuss Dummy encoding in the context of feature engineering
Q. What is Feature Engineering?
Q. What do you mean by noise in the dataset?
Q. What is Nearest Neighbor Imputation?
Q. What is Extreme Value Imputation?
Q. What is Mode Imputation?
Q. What is Mean Imputation?
Q. What is Discretization? When is doing discretization better as opposed to using continuous variable?
Q. What is Feature Binarization? When to use feature binarization?

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Explore Questions by Topics

Computer Vision (1)
Generative AI (2)
Machine Learning Basics (18)
Deep Learning (52)
- DL Basics (16)
- DL Architectures (17)
  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Training and Optimization (17)
Natural Language Processing (27)
- NLP Data Preparation (18)
Supervised Learning (115)
- Regression (41)
  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
- Classification (70)
  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (55)
- Clustering (37)
  - Distance Measures (9)
  - K-Means Clustering (9)
  - Hierarchical Clustering (3)
  - Gaussian Mixture Models (5)
  - Clustering Evaluations (6)
- Dimensionality Reduction (9)
Statistics (34)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)

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Other Questions in Feature Engineering

What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
What are the subtypes of Cross Validation?
What is Specificity?
Explain the concept and working of the Random Forest model
How does a learning curve give insight into whether the model is under- or over-fitting?
What is the difference between Discriminative and Generative models?

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