Overview

Multiple linear regression is very similary to single variable regression, the only signficant difference is the shape of the inputs.

Example with Multiple Variables

The following demonstrates running a 2 variable regression, using hard coded data.

	await $src('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js');
	
	let  linearModel = tf.sequential();
	linearModel.add(tf.layers.dense({units:1, inputShape:[2]}));
	linearModel.compile({loss:'meanSquaredError',optimizer:'sgd'});
	
	
	let xs = tf.tensor([[3.2,1],[4.4,1],[5.5,2],[6.71,0.8],[7.168,1.2],[9.779,5],[6.182,5],[7.59,4.5],[2.16,7]]);
	let ys = tf.tensor([[1.6],[2.7],[2.9],[3.19],[1.684],[2.53],[3.366],[2.596],[2.53]]);
	
	await linearModel.fit(xs,ys,{
	epochs:80
	});
	
	let output = linearModel.predict(tf.tensor([4,6]));
	prediction = Array.from(output.dataSync())[0];
	

Try it!

No Intercept

In order to specify that the model not use a bias (or intercept term), you specify the property useBias as false in the model.add method.

	await $src('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js');
	
	var  linearModel = tf.sequential();
	linearModel.add(tf.layers.dense({units:1, inputShape:[2], useBias: false}));