Pre-processing (data preparation)

Partition the data into training set (80%) and test set (20%).

Model evaluation

Apply the trained Linear Regression to the test set with the "Regression Predictor" node. Evaluate the prediction using the "Numeric Scorer"node.

Model training

Train the Linear Regression with the "Linear Regression Learner" node.

Read data

The data contains various attributes about different houses and their price.

How to train a Linear Regression model?

Step 1: Drag the "Linear Regression Learner" node into the workflow and click on it to open the configuration window.

Step 2: Select the "Target" column as "SalesPrice".

Step 3: Execute the node. Investigate the view of the regression coefficients (click magnifier in the node action bar).

How to evaluate a Linear Regression model?

Step 1: Drag the "Regression Predictor" node into the workflow.

Step 2: Connect the output of "Linear Regression Learner" node to Port 0 andthe test set to Port 1. Execute the node.

Step 3: Connect the output containing the predictions to the "Missing Value" node to remove rows with missing prediction. Then, connect it to "Scorer" node to evaluate the model on various evaluation measures.

Analyze Data: Training a Linear Regression

This workflow is an example of how to train and evaluate a basic machine learning model for a house price prediction task.

In this case, we train and apply a Linear Regression algorithm. However, the Learner-Predictor construct is common to all supervised algorithms.

Workflow complete!

Keep the momentum going by exploring Just KNIME It! on the Hub to challenge yourself and see how these nodes can be integrated into more complex workflows and use cases.