What is Feature Engineering in Machine Learning?
Feature Engineering is the process of selecting, transforming, and creating variables (features) that help machine learning models perform better.
A model is only as good as the data you feed it. Even the best algorithm will fail with poor features.
Common feature engineering techniques:
Handling missing values
Encoding categorical variables (One-Hot Encoding, Label Encoding)
Feature scaling (Normalization, Standardization)
Creating new features from existing ones
Good feature engineering can significantly improve model accuracy without changing the algorithm.
What is Cross-Validation and Why is it Important?
Cross-validation is a technique used to evaluate how well a machine learning model performs on unseen data.
Instead of splitting data once into training and testing sets, cross-validation splits the data multiple times to ensure reliability.
The most common method is K-Fold Cross-Validation:
Data is divided into K parts.
The model trains on K-1 parts and tests on the remaining part.
This process repeats K times.
Benefits:
Reduces overfitting risk
Provides more reliable performance estimates
Helps choose better models
Cross-validation is essential for building robust ML systems.
What is Regularization in Machine Learning?
Regularization is a technique used to prevent overfitting by adding a penalty to complex models.
When a model becomes too complex, it may memorize training data instead of learning patterns.
Two popular regularization methods:
L1 Regularization (Lasso) – Can shrink some coefficients to zero.
L2 Regularization (Ridge) – Reduces coefficient magnitude without eliminating them.
Regularization helps balance model complexity and performance.
Introduction to Decision Trees in Machine Learning
Decision Trees are supervised learning algorithms used for both classification and regression.
They work by splitting data into branches based on feature values.
Why Decision Trees are popular:
Easy to understand and visualize
Requires little data preparation
Works with both numerical and categorical data
However, they can overfit easily. That’s why ensemble methods like Random Forest are often preferred.
Decision Trees are a great starting point for beginners in ML.
