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The log-likelihood for this model is$$\ln L(\theta) = \sum_{i=1}^n \Big[ y_i \ln \Phi (x_i\theta) + (1 - y_i) \ln (1 - (x_i\theta)) \Big] $$Congratulations! After today's blog, you should have a better understanding of the fundamentals of maximum likelihood estimation. The point estimate depends on the type of data:One example
In mathematical notation, if \(\hat{y}\) is the predicted value. For high-dimensional datasets with many collinear features, LassoCV is most often preferable. The extension to multiple and/or vector-valued predictor variables (denoted with a capital X) is known as multiple linear regression, also known as multivariable linear regression (not to be confused