Regression with Lasso Algorithm

Regression with the Lasso algorithm is a method for solving regression problems in machine learning. It is a linear regression model that includes L1 regularization, which is a technique that adds a penalty term to the loss function to reduce the complexity of the model. The Lasso algorithm aims to find the simplest and most interpretable model, by setting some of the coefficients of the model to zero.

The Lasso algorithm starts by defining a linear model with L1 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. The objective of Lasso is to minimize the sum of the residuals and the L1-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 Lasso 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, etc.

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

It is important to note that Lasso algorithm tends to give sparse solutions, so it might be useful for feature selection. Also, Lasso 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 Lasso algorithm is a linear regression model that includes L1 regularization, which is a technique that adds a penalty term to the loss function to reduce the complexity of the model. It aims to find the simplest and most interpretable model by setting some of the coefficients of the model to zero. Lasso algorithm tends to give sparse solutions, so it might 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 Lasso Algorithm in regression problems.