Create a computer vision system using decision tree algorithms to solve a real-world problem : Logistic Regression

The video tutorial explains logistic regression as a method for binary classification, similar to linear regression but with predictions transformed into probabilities using the sigmoid function. It highlights the use of logistic regression in neural networks and discusses the necessity of gradient descent and maximum likelihood for model optimization. The tutorial concludes with a practical application of logistic regression on real data.

What is the significance of the output range in logistic regression?

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Describe how gradient descent is used in logistic regression.

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