5 Questions
What is the primary goal of regression analysis in modeling the relationship between dependent and independent variables?
To minimize the difference between observed and predicted values.
What is a common challenge in regression analysis, and how can it be addressed?
Outliers; proper handling through techniques such as data cleaning, normalization, and robust regression methods.
What is the primary goal of classification models, and how do they achieve it?
To assign accurate labels to new data; through learning from a training dataset with known input features and class labels.
Why is logistic regression preferred when the target variable is binary?
Because logistic regression is specifically designed to handle binary dependent variables and provides probabilities of belonging to each class.
What is the primary difference between regression and classification problems?
Regression predicts continuous values, while classification predicts discrete labels or categories.
Study Notes
Machine Learning Algorithms
- Three main categories of ML algorithms: Classification, Regression, and Clustering
- Examples of algorithms: Logistic Regression, Linear Regression, K-Means, SVM, Naïve-Bayes, Nearest Neighbor, Decision Trees, Random Forests, and Hidden Markov Model
Regression Problem
- Regression analysis models the relationship between a continuous dependent variable and one or more independent variables
- Goal: find the best-fit line that minimizes the difference between observed and predicted values
- Regression is sensitive to outliers, which can distort the model
- Proper handling of outliers is crucial for accuracy and reliability of the regression model
Classification Problem
- Classification predicts the category or class of an input based on prior observations, producing discrete labels (e.g., "spam" or "not spam")
- Goal: assign accurate labels to new data
- Popular algorithms: logistic regression and decision trees
- Classification involves input features and class labels (target) in the training dataset
This quiz covers machine learning algorithms, including classification, regression, and clustering techniques. Topics include logistic regression, linear regression, k-means, SVM, and more.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
