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PENDUGAAN PARAMETER REGRESS LOGISTIC MULTINOMIAL MENGGUNAKAN METHOD The LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) (Studio ASUS Lilian Scholar SMA Pederast Ole SISA SVP Dan MT's Never
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Decide on the value of the shrinking parameter (lambda) based on the level of regularization required.
02
Define the loss function and optimization algorithm to be used for training.
03
Fit the model using the training data by minimizing the sum of the loss function and regularization term scaled by lambda.
04
Use the trained model to make predictions on new data.
05
Evaluate the performance of the model using metrics such as mean squared error or R-squared.

Who needs metode least absolute shrinkage?

01
Individuals or organizations working with high-dimensional data or data prone to multicollinearity.
02
Researchers or practitioners looking for a method to perform variable selection while handling correlated predictors.
03
Those interested in balancing model interpretability and prediction accuracy in their analysis.
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Metode least absolute shrinkage is a statistical method used for shrinkage estimation in regression analysis, where the least absolute deviation is minimized to improve the accuracy of the model.
Researchers, statisticians, and analysts who are conducting regression analysis and want to improve the model's predictive accuracy may use the metode least absolute shrinkage.
Metode least absolute shrinkage is typically implemented using software or programming languages that have built-in functions for regularization techniques, such as Lasso regression.
The purpose of metode least absolute shrinkage is to improve the accuracy and generalization of regression models by penalizing the coefficients of the predictors to prevent overfitting.
The results of the regression analysis using metode least absolute shrinkage include the estimated coefficients of the predictors and the shrinkage parameter that determines the level of regularization applied.
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