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Data Preparation

Q. What are some of the most common feature engineering techniques?
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 the difference between Feature Engineering and Feature Selection?
Q. What is Feature Standardization (or Z-Score Normalization), and why is it needed?
Q. What is bootstrapping, and why is it a useful technique?
Q. Discuss Discretization in the context of feature engineering
Q. What is the difference between probability and non-probability sampling, and what are some example methodologies for each?
Q. Among the common machine learning algorithms, which require feature scaling, and which do not?
Q. What are the different categories of missing data?
Q. What are different ways to impute missing values for a feature?
Q. What does Centering and Scaling mean? What is the individual effect of each of those?
Q. How are categorical features or qualitative predictors represented in a machine learning model?
Q. What is MinMax Normalization? Compare MinMax Normalization with Z-Score Standardization
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 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 Cluster Sampling?
Q. What is Stratified Sampling?
Q. What is Simple Random Sampling?
Q. What is Data Sparsity?
Q. What is Data Leakage?
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 do you mean by noise in the dataset?
Q. What is Feature Engineering?
Q. What is Normalization?
Q. How to perform Standardization in case of outliers?
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?
Q. What is Nearest Neighbor Imputation?
Q. What is Extreme Value Imputation?
Q. What is Mode Imputation?
Q. What is Mean Imputation?

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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 Data Preparation

Explain the concept of Linear Regression
How are coefficients of linear regression estimated?
What are the assumptions of linear regression?
How is variability measured in Linear Regression?
What are the evaluation criteria for a Linear Regression model?
How can categorical predictors be incorporated in linear regression?

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