In order to train a logistic regression model, we find the weights that maximize the likelihood of the model.Group of answer choicesTrueFalse

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In logistic regression, we aim to find the parameter weights that maximize the likelihood of the observed data given the model. This is based on the concept of maximum likelihood estimation (MLE). The logistic regression model estimates the probability of a binary outcome based on one or more predictor variables by modeling the log odds of the outcome as a linear combination of the predictors.

Here's a brief explanation of how it works:

1. Likelihood Function: The likelihood function is derived from the Bernoulli distribution because logistic regression deals with binary outcomes (0 or 1). The likelihood function calculates the probability of observing the given outcomes based on the predicted probabilities.

2. Maximization: To find the model parameters (weights), we maximize the likelihood function. This can be done using optimization techniques such as gradient ascent or other numerical methods.

3. Convergence: Once the optimization process converges, the resulting weight estimates represent the most probable parameters for the logistic model given the data.

Overall, maximizing the likelihood is a fundamental principle in logistic regression, allowing the model to make accurate predictions based on the training data.