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Linear Algebra for Data Science Using Python

Multi-target Linear Regression

Explore the concept of multi-target linear regression where multiple output variables are predicted simultaneously from input features. Understand matrix formulations, the Frobenius norm, and how to implement and evaluate this model using Python and sklearn. Gain practical skills in handling multi-output data sets for real-world data science problems.

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Multi-target data sets

It’s common in real applications to predict more than one target from given features. The data setUCI_Dataset we’ve discussed in the previous lesson comes from a similar scenario. We’ve already approximated a single target given its multiple features. In this lesson, we’ll predict both Y1 (heating load) and Y2 (cooling load), given X1 to X8 features. We can do so using multi-target linear regression. Formally, if x1,x2,...,xd\bold{x_1},\bold{x_2},...,\bold{x_d} are the feature columns and y1,y2,...,yc\bold{y_1},\bold{y_2},...,\bold{y_c} ...