Discover how logistic regression excels in classification tasks, assigning items to categories with precision. Learn why it’s the top choice for machine learning models.
Table of Contents
Question
Which technique should you use to implement a classification model that can assign items to categories?
A. Ridge Regression
B. Lasso Regression
C. Logistic Regression
D. Linear Regression
Answer
C. Logistic Regression
Explanation
Logistic regression is used to implement a classification model that can assign items to categories. It is a common classification algorithm and uses a sigmoid function to transform its linear output into probabilities between 0 and 1. These probabilities represent the likelihood of an item belonging to each category. Based on a predefined threshold, the model assigns the item to the category with the highest probability.
Linear regression is not used to implement a classification model. It excels at modeling the relationship between features (independent variables) and a target variable (dependent variable) that can take on any numerical value within a range. Classification models, on the other hand, aim to assign data points to discrete categories. For example, classifying emails as spam or not spam, or classifying images as containing cats or dogs. Here, the output belongs to a predefined set of categories, not a continuous range of numerical values.
Lasso and ridge regression are both regularization techniques used within the context of linear regression to address overfitting, not separate classification algorithms. While they can improve the performance of linear regression models, they do not directly perform classification.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.