Applied Data Science Coding with Python: Regression with ElasticNet Algorithm
The ElasticNet algorithm starts by defining a linear model with a combination of L1 and L2 regularization. L1 regularization is also known as Lasso regularization, it adds “absolute value of magnitude” of coefficient as a penalty term to the loss function. L2 regularization is also known as Ridge regularization, it adds “squared magnitude” of coefficient as a penalty term to the loss function. The combination of these two regularization techniques helps to balance the trade-off between preserving the sparsity of the model and minimizing the model’s complexity.
After the model is trained, it can be used to make predictions for new data points by passing the new input data through the model. The predicted target variable value is the output of the model for the new data point.
In order to use the ElasticNet algorithm for regression in Python, you need to have a dataset that includes both the input data and the target variable values. You also need to decide on the parameters such as the regularization strength, the ratio between L1 and L2 regularization, etc.
There are several libraries available in Python to implement the ElasticNet algorithm for regression, such as scikit-learn, NumPy, and Pandas. These libraries provide pre-built functions and methods to build, train, and evaluate an ElasticNet model for regression.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to apply ElasticNet Algorithm in regression problems.