Regression with Ridge Algorithm

Regression with the Ridge algorithm is a method for solving regression problems in machine learning. It is a linear regression model that includes L2 regularization, which is a technique that adds a penalty term to the loss function to reduce the complexity of the model.

The Ridge algorithm starts by defining a linear model with L2 regularization. L2 regularization is also known as Ridge regularization, it adds “squared magnitude” of coefficient as a penalty term to the loss function. The objective of Ridge is to minimize the sum of the residuals and the L2-norm of the coefficients.

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 Ridge algorithm for regression, 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, etc.

There are several libraries available in Python to implement the Ridge algorithm for regression, such as scikit-learn, NumPy, and Pandas. These libraries provide pre-built functions and methods to build, train, and evaluate a Ridge model for regression.

It’s important to note that Ridge algorithm tends to give non-sparse solutions, so it might not be useful for feature selection. Also, Ridge algorithm might be sensitive to the scale of the features, so it’s important to scale the features before using the algorithm.

In summary, Regression with the Ridge algorithm is a method for solving regression problems in machine learning. It is a linear regression model that includes L2 regularization, which is a technique that adds a penalty term to the loss function to reduce the complexity of the model. The Ridge algorithm can be implemented using several libraries available in Python, and it’s important to note that Ridge algorithm tends to give non-sparse solutions, so it might not be useful for feature selection and it might be sensitive to the scale of the features, so it’s important to scale the features before using the algorithm.

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 Ridge Regression Algorithm in regression problems.