📉The cost function in machine learning measures the error in model predictions.
📊The cost function in logistic regression is different from linear regression.
🎢The cost function graph for logistic regression has local minima and global minima.
📝The cost function in logistic regression uses the sigmoid function to calculate the error for each observation.
⚙️Gradient descent is used to minimize the cost function in logistic regression.
What does the cost function measure?
—The cost function measures the error in the model's predictions.
How is the cost function different in logistic regression?
—The cost function in logistic regression is designed for binary classification.
Why does the cost function in logistic regression have local and global minima?
—The structure of the cost function graph for logistic regression causes multiple minima.
How is the error calculated for each observation in logistic regression?
—The error for each observation is calculated using the sigmoid function.
What is gradient descent?
—Gradient descent is an algorithm used to minimize the cost function in logistic regression.
00:12The cost function measures the error in the model's predictions.
01:00The cost function in logistic regression is different and helps us reach the global minimum.
02:26The cost function graph for logistic regression has local minima and global minima.
03:36The error for each observation in logistic regression is calculated using the sigmoid function.
05:02Gradient descent is used to minimize the cost function in logistic regression.
