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Understanding Linear Regression Models in Machine Learning
Linear models are fundamental predictive tools that establish linear relationships between variables using the equation y = mx + c, optimized through gradient descent and MSE. The model's effectivenes
s is enhanced through regularization techniques like Ridge and Lasso to address common challenges such as overfitting and high dimensionality.

Reasons to Read -- Learn:

  • fundamental principles of linear regression models, including how the cost function and gradient descent work together to optimize model parameters for accurate predictions.
  • common challenges in linear modeling such as overfitting and underfitting, and understand how to identify these issues in your own machine learning projects.
  • practical solutions through regularization techniques (Ridge and Lasso), including how they differ in their approach to coefficient handling and feature selection.
  • 4 min readauthor: Tanisha.Digital
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