Understand the crucial differences between linear and logistic regression models. Explore output ranges, curve shapes, and more. Prepare for the IBM AI Fundamentals certification exam.
Table of Contents
Question
Which of the following is an important difference between linear and logistic regressions?
A. Only linear regressions have outputs shaped like an S-curve.
B. Only linear regressions have outputs that cannot be resolved.
C. Only logistic regressions follow branching logic paths to an answer.
D. Only logistic regressions have outputs between 0 and 1.
Answer
The most important difference between linear and logistic regression is:
D. Only logistic regressions have outputs between 0 and 1.
Explanation
A distinction about logistic regressions is that they are usually used to produce an output value that ranges between 0 and 1.
Linear regression is used for predicting continuous numerical values. Its outputs can take on any value along the real number line, from negative infinity to positive infinity. The goal is to find the best-fitting straight line through the data points.
In contrast, logistic regression is designed for binary classification problems, where the output is a probability between 0 and 1. The logistic function, also known as the sigmoid function, maps any real-valued number to a value between 0 and 1. This output can be interpreted as the probability of the input belonging to a particular class.
While both linear and logistic regression models can have S-shaped curves (option A is incorrect), and both can have outputs that are resolvable (option B is incorrect), only logistic regression strictly outputs values between 0 and 1.
Additionally, logistic regression does not follow branching logic paths (option C is incorrect). It applies a linear combination of input features and then passes the result through the logistic function to obtain the final probability output.
Understanding these key differences is crucial for selecting the appropriate regression model based on the problem at hand and for accurately interpreting the model’s outputs.
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