Hits: 82

# 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.

## Regression with Ridge Algorithm

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**

**What is Ridge Regression Algorithm?**

**Ridge Regression** is a technique for analyzing multiple **regression** data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value.

How to create and optimise a baseline Ridge Regression Model in Python