Google trend - Lasso

Lasso - 10 things to know with detail

• Lasso is a machine learning algorithm that is used for regression and classification tasks. It is particularly useful for feature selection and regularization.
• Lasso stands for Least Absolute Shrinkage and Selection Operator. It works by adding a penalty term to the standard linear regression cost function, which penalizes the absolute size of the coefficients.
• The main advantage of Lasso is its ability to perform feature selection by shrinking some coefficients to zero. This helps in reducing overfitting and improving model interpretability.
• Lasso is particularly useful when dealing with high-dimensional data, where the number of features is much greater than the number of samples. It helps in identifying the most important features while discarding irrelevant ones.
• Lasso is a linear model, which means it assumes a linear relationship between the features and the target variable. It is suitable for datasets where the relationship is approximately linear.
• Lasso can be used for both regression and classification tasks. In regression, it minimizes the residual sum of squares, while in classification, it minimizes the logistic loss function.
• Lasso uses L1 regularization, which adds the absolute values of the coefficients to the cost function. This encourages sparsity in the model, leading to a simpler and more interpretable solution.
• One limitation of Lasso is that it tends to select only one feature from a group of highly correlated features. This can be addressed by using other regularization techniques such as Elastic Net, which combines L1 and L2 regularization.
• Lasso requires tuning of the regularization parameter, also known as the alpha parameter. This controls the amount of regularization applied to the model, with higher values leading to more shrinkage and sparsity.
• Lasso can be implemented using various machine learning libraries such as scikit-learn in Python. It is a powerful tool for feature selection and regularization, and is widely used in various fields such as finance, healthcare, and marketing.