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What is Lasso Regression?
Lasso Regression is a machine learning technique used for regression analysis that performs both variable selection and regularization in order to improve the prediction accuracy and interpretability of the statistical model it produces.
How does Lasso Regression work?
Lasso stands for Least Absolute Shrinkage and Selection Operator. It works by adding a penalty term to the ordinary least squares regression equation, which penalizes the absolute value of the coefficients. This penalty term encourages the algorithm to shrink the coefficients for some variables to zero, effectively performing variable selection.
The objective function of Lasso Regression can be represented as:
min ||Y – Xβ||^2 + λ||β||1
Where:
- Y is the target variable
- X is the feature matrix
- β is the coefficient vector
- λ is the regularization parameter
Benefits of Lasso Regression
1. Feature Selection: Lasso Regression automatically selects the most relevant features by shrinking the coefficients of irrelevant features to zero.
2. Interpretability: By reducing the number of features in the model, Lasso Regression makes it easier to interpret the relationships between variables.
3. Regularization: Lasso Regression helps prevent overfitting by adding a penalty term to the regression equation.
Implementation in Python
Here is an example of how to implement Lasso Regression in Python using the scikit-learn library:
“`python
from sklearn.linear_model import Lasso
lasso_reg = Lasso(alpha=0.1)
lasso_reg.fit(X_train, y_train)
“`
Conclusion
Lasso Regression is a powerful tool for performing regression analysis while simultaneously selecting important features and preventing overfitting. By adding a penalty term to the regression equation, Lasso Regression encourages sparsity in the coefficients, making it a useful technique for data scientists and machine learning practitioners.
