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 and double click to open the dialog.

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

Step 3: RIght Click on the node and select "Execute and Open View" to train the model and to get a view of the Regression Co-efficients.

How to evaluate a Linear Regression model?

Step 1: Drag the "Regression Predictor" node.

Step 2: Connect the output of "Linear Regression Learner" node to model input port and Test Dataset to data input port. Execute the node.

Step 3: Connect the Predictor Output to "Missing Value" node to remove rows with missing prediction and then connect it to "Scorer" node to evaluate the model on various evaluation measures.

Analyze Data by 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.