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Questions and Answers
What is the primary goal of a classification problem?
Which regression algorithm is suitable for a binary dependent variable?
What is a common issue with regression analysis?
Which algorithm is used for clustering data?
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What is the primary difference between regression and classification problems?
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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
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Description
This quiz covers various machine learning algorithms, including classification, regression, and clustering techniques. Test your knowledge of logistic regression, linear regression, SVM, and more.
