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LASSO regression in Excel

This tutorial explains how to set up and interpret LASSO regression in Excel using the XLSTAT statistical software.

Dataset for LASSO regression

The original data are from Osborne and al. (1984). The dataset contains, for 30 cookies, the discretizations of near infrared spectra. The spectrum is observed at all wavelengths between 1100 and 2460 nanometers, with a spacing of 40 nanometers between each wavelength, which makes 35 explanatory variables in the dataset. The data table also contains the water composition of each cookie.

The goal is to predict the composition of the different cookies in water.

Goal of this tutorial

The goal of this tutorial is to set up and interpret a LASSO regression on the Cookies dataset.

Setting up a LASSO regression in XLSTAT

Once XLSTAT is open, click on Modeling / LASSO regression as shown below:

The LASSO regression dialog box appears.

You can then select the data on the Excel sheet. The Dependent variable (or variable to model) is here the water composition of cookies.

The Quantitative explanatory variables correspond to all columns numbered L1 to L35. Here we want to explain the variability of the water content in the cookies as a function of the spectrum.

Only the first 20 cookies of the dataset are selected to participate in the modeling, the others will be used for the prediction.

As the name of each variable is displayed at the top of the table, we must check the Variable labels checkbox.

You can also check the Observation labels box and select the name of the different cookies.

In the Options tab, we use Cross-validation to find the optimal lambda regularization parameter. We choose to form 5 folds for the cross-validation and to test 100 lambda values.

In the Prediction tab, select the data on which you want to get a prediction. Here we select the last 10 cookies in the dataset.

Configure the Outputs tab as follows:

In the Charts tab, you can activate the option Evolution of the MSE (Cross-validation) which allows you to have access to the evolution of the MSE (Mean Squared Error) according to the regularization parameter.

The computations begin once you have clicked on the OK button.

Interpreting a LASSO regression

The table “Model parameters” gives details on the model. This table is helpful when predictions are needed or to analyze the importance of variables in the model. We see that only 9 of the 35 variables were kept in the modeling, the others have a zero coefficient.

The following chart shows the evolution of the MSE according to the Lambda regularization parameter. The Lambda value selected by XLSTAT is the one that minimizes the MSE, here the value 0.01.

The last table gives, following the estimation of the model, the predictions of the water composition of the 10 cookies composing the prediction dataset.


Conclusion on LASSO regression

Finally, LASSO regression modeled the water composition of the different cookies with only 9 of the 35 available variables: the other variables were judged not to be explanatory enough or to contain information too similar to a variable already selected.

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