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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