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sanjay-kv
Is there an existing issue for this?
Feature Description
This code is an implementation of a basic linear classifier that attempts to train weights (parameters) to classify data based on given input features. It follows a Perceptron-like structure, which is a simple type of neural network with a linear decision boundary. Here's a breakdown of what the code does and its applications:
Use Case
Binary Classification: The code classifies data into two categories. This approach can be used in basic binary classification tasks, such as spam detection, disease prediction, and fraud detection.
Perceptron Model Training: This code demonstrates how a simple linear classifier, like the Perceptron, can be trained to adjust its weights using errors from predictions, making it a foundational step for understanding neural networks and deep learning.
Error Analysis & Convergence: By observing the loss curve, you can see whether the model converges (i.e., reduces errors over time) or if it diverges, indicating potential issues with learning rate or data separability.
Medical Diagnosis: In this example, the model uses features like Glucose and BMI to predict diabetes presence. Although simplistic, this can be a useful prototype model for binary classification in healthcare. It provides a starting point for understanding how multiple features contribute to a prediction, paving the way for more complex models.
Benefits
@sanjay-kv Sir please assign me this task. I want to add it under classification models
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Priority
High
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