Q&A: 2
Advanced Concepts - Questions and Answers
Advanced Concepts
What is logistic regression, and when is it used?
Logistic regression is a classification algorithm used to predict the probability of a binary outcome (e.g., 0 or 1, yes or no). It is often used when the dependent variable is categorical. The algorithm applies the logistic function to map predicted values to probabilities.
Explain Principal Component Analysis (PCA) and its purpose.
Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into fewer dimensions by identifying the principal components. The main purpose is to reduce complexity, improve visualization, and remove noise while retaining as much variance as possible.
What is ensemble learning, and what are its types?
Ensemble learning is a technique that combines multiple models to improve performance. It aims to increase accuracy, reduce overfitting, and enhance generalization. The main types of ensemble methods are bagging (e.g., Random Forest), boosting (e.g., AdaBoost, XGBoost), and stacking.
What is deep learning, and how is it different from traditional machine learning?
Deep learning is a subset of machine learning that uses neural networks with multiple layers (deep neural networks) to model complex patterns in large datasets. Unlike traditional machine learning models, deep learning can automatically extract features from raw data, making it suitable for tasks like image recognition, natural language processing, and speech recognition.
What is feature engineering, and why is it important?
Feature engineering is the process of creating, transforming, or selecting relevant features from raw data to improve model performance. It is crucial because well-crafted features help models make better predictions, increase accuracy, and capture important patterns in the data.