๐ฃ๐ฒ๐ฟ๐ฐ๐ฒ๐ฝ๐๐ฟ๐ผ๐ป ๐๐. ๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป: ๐๐ฒ๐ ๐๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐ ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป๐ฒ๐ฑ
The ๐ฃ๐ฒ๐ฟ๐ฐ๐ฒ๐ฝ๐๐ฟ๐ผ๐ป and ๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป algorithms share ๐บ๐ฎ๐ป๐ ๐๐ถ๐บ๐ถ๐น๐ฎ๐ฟ๐ถ๐๐ถ๐ฒ๐, but understanding their differences is crucial.
The ๐ฃ๐ฒ๐ฟ๐ฐ๐ฒ๐ฝ๐๐ฟ๐ผ๐ป ๐ฎ๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ relies on a ๐๐๐ฒ๐ฝ ๐ณ๐๐ป๐ฐ๐๐ถ๐ผ๐ป for classification, updating weights only when a prediction is incorrect. It follows a simple rule-based approach, making it effective for linearly separable data but limited in handling more complex scenarios.
In contrast, ๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป utilizes the ๐๐ถ๐ด๐บ๐ผ๐ถ๐ฑ ๐ณ๐๐ป๐ฐ๐๐ถ๐ผ๐ป and follows a probabilistic approach based on maximum likelihood estimation. Instead of making direct class predictions, it calculates probabilities, allowing for more nuanced decision-making.
A key distinction is how these models update their weights. The ๐ฃ๐ฒ๐ฟ๐ฐ๐ฒ๐ฝ๐๐ฟ๐ผ๐ป ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ ๐๐ฒ๐ถ๐ด๐ต๐๐ only when a ๐บ๐ถ๐๐ฐ๐น๐ฎ๐๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ฐ๐ฐ๐๐ฟ๐, whereas ๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป adjusts its weights in every iteration based on the ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐ฏ๐ฒ๐๐๐ฒ๐ฒ๐ป ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ฒ๐ฑ ๐ฝ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐ฎ๐ป๐ฑ ๐ฎ๐ฐ๐๐๐ฎ๐น values. This continuous optimization makes Logistic Regression more robust and adaptable.
For a detailed understanding of Perceptron and Logistic Regression, check out these videos:
1๏ธโฃ Towards Logistic Regression - Perceptron Algorithm | First Classification Algorithm
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2๏ธโฃ Logistic Regression Simplified: Your First Step into Classification | Intuitive Approach
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3๏ธโฃ Loss Function for Logistic Regression | Negative Log Likelihood | Log(Odds) | Sigmoid
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