Today I continued my Machine Learning journey by learning about Cost Functions for Logistic Regression.
One of the key questions I explored was:
How does a machine learning model know if its predictions are good or bad?
The answer is the Cost Function.
The cost function measures the error made by the model. A lower cost means better predictions, while a higher cost indicates more mistakes.
I also learned that Logistic Regression uses a special type of error measurement called Logistic Loss (Log Loss). This loss function heavily penalizes incorrect predictions and rewards correct ones.
Another important takeaway was understanding how Gradient Descent uses the cost function to improve the model step by step.
To reinforce my understanding, I completed both labs and the practice quiz, achieving a score of 100%.
While some of the mathematics still requires practice, I now have a clearer understanding of how machine learning models evaluate and improve their performance.
🚀 Every lesson helps me build a stronger foundation in Machine Learning.
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