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Databytes on Instagram: "When to use different regularization techniques in machine learning becomes critical once logistic regression starts overfitting in high dimensional data. As features increase, logistic regression can memorize noise instead of learning patterns. L2 regularization, also called Ridge, shrinks coefficients smoothly and controls model complexity without eliminating features. L1 regularization, known as Lasso, pushes weak coefficients to zero and performs feature selection au
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Databytes on Instagram: "When to use different regularization techniques in machine learning becomes critical once logistic regression starts overfitting in high dimensional data. As features increase, logistic regression can memorize noise instead of learning patterns. L2 regularization, also called Ridge, shrinks coefficients smoothly and controls model complexity without eliminating features. L1 regularization, known as Lasso, pushes weak coefficients to zero and performs feature selection au
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