Question

How is logistic regression different from the joint probabilities approach?

Option 1: Uses a linear decision boundary
Option 2: Assumes feature independence
Option 3: Incorporates a sigmoid function
Option 4: Requires joint probability calculations

Answer 1

Logistic regression and the joint probabilities approach differ in terms of their decision boundary, assumption of feature independence, incorporation of a sigmoid function, and requirement of joint probability calculations.

Step-by-step explanation:

Logistic regression and the joint probabilities approach are both statistical methods used to analyze data and make predictions. However, they differ in several key aspects:

1. Logistic regression uses a linear decision boundary to classify data into different categories. It fits a logistic curve to the data and determines the probability of an event occurring.
2. The joint probabilities approach, on the other hand, assumes that the features (independent variables) are independent of each other. It calculates the joint probability of all the features occurring together.
3. Logistic regression incorporates a sigmoid function, also known as a logistic function. This function transforms the linear decision boundary into a curve, allowing for non-linear relationships between the independent variables and the dependent variable.
4. Unlike the joint probabilities approach, logistic regression does not require the calculation of joint probabilities but rather uses maximum likelihood estimation to estimate the model parameters.