Exploring Supervised Learning Techniques

What is the primary goal of linear regression?

• To predict future values without any data

• To model the relationship between variables.

• To minimize the number of variables used

• To create complex non-linear models

Explain the difference between supervised and unsupervised learning.

• Supervised learning is only used for classification tasks.

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

• Supervised learning is faster than unsupervised learning.

• Unsupervised learning requires more data than supervised learning.

What type of problems is logistic regression used for?

• Logistic regression is used for binary classification problems.

• Clustering problems

• Regression analysis for continuous outcomes

• Time series forecasting

Describe how the KNN algorithm classifies data points.

• KNN classifies data points by random selection.

• The KNN algorithm classifies data points based on the majority class of their 'k' nearest neighbors.

• KNN uses a single farthest neighbor to classify points.

• KNN assigns classes based on the average distance to all points.

What does SVM stand for and what is its main purpose?

• Support Vector Method

• Support Vector Model

• Supervised Vector Machine

• Support Vector Machine

Compare decision trees and random forests in terms of accuracy.

• Random forests have lower accuracy than decision trees in all cases.

• Decision trees are always more accurate than random forests.

• Decision trees and random forests have the same level of accuracy.

• Random forests are generally more accurate than decision trees.

What is the equation of a simple linear regression model?

• y = mx + b

• y = m + bx

• y = ax^2 + b

• y = mx^2 + c

How does the KNN algorithm determine the nearest neighbors?

• It averages the coordinates of all points to determine proximity.

• The KNN algorithm determines the nearest neighbors by calculating distances and selecting the closest 'k' points.

• It uses a decision tree to find neighbors.

• It randomly selects points without considering distance.

What are the advantages of using logistic regression?

• Requires large datasets for accuracy

• High complexity in model interpretation

• Inability to handle categorical variables

• Advantages of using logistic regression include simplicity, interpretability, efficiency, probability outputs, and suitability for binary outcomes.

In what scenarios would you prefer SVM over KNN?

• When computational efficiency is the top priority

• When the data is not linearly separable

• When working with small datasets and noisy data

• When dealing with high-dimensional data, limited data, need for clear margins, or larger datasets.