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Explain Gradient Decent Rules and Error correction.
Explain Gradient Decent Rules and Error correction.
Gradient Descent is an optimization algorithm used to minimize the error function in machine learning models. It works by iteratively adjusting the model's parameters in the direction of steepest descent. Error correction refers to the process of updating the model's parameters based on the calculated error between the predicted output and the actual output during training.
Which of the following best describes Gradient Descent?
Which of the following best describes Gradient Descent?
- A way to calculate the error of a machine learning model
- A method used to minimize the error of a machine learning model by adjusting its parameters (correct)
- A process of randomly selecting data points to train a machine learning model
- A technique used to maximize the accuracy of a machine learning model by adjusting its parameters
What is the purpose of Error Correction in machine learning?
What is the purpose of Error Correction in machine learning?
- To maximize the accuracy of a machine learning model
- To calculate the error of a machine learning model
- To randomly select data points for training
- To minimize the error of a machine learning model (correct)
How does Gradient Descent help in error correction?
How does Gradient Descent help in error correction?