47.What is the loss function used in Logistic Regression to find the best fit line?  A. Log-Loss  B. MSE  C. MAE  D. MAPE

The loss function used in Logistic Regression to find the best fit line is A. Log-Loss.

Here's why:

Logistic Regression is a classification algorithm, it predicts a probability between 0 and 1. To measure how well the algorithm performs, we need a way to compare the predicted probabilities to the actual class labels in the training data. This is where the Log-Loss function comes in.

The Log-Loss function, also known as logistic loss or cross-entropy loss, is commonly used in logistic regression and neural networks. It provides a measure of error for the predicted probabilities of the actual class labels. The goal of logistic regression is to minimize this error.

The other options, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are typically used for regression problems, not classification problems like logistic regression.