We will start this chapter by implementing nearest-neighbors to predict housing values. This is a great way to start with nearest-neighbors, because we will be dealing with numerical features and continuous targets.
Working with nearest-neighbors
Getting ready
To illustrate how making predictions with nearest-neighbors works in TensorFlow, we will use the Boston housing dataset. Here, we will be predicting the median neighborhood housing value as a function of several features.
Since we consider the training set the trained model, we will find the k-NNs to the prediction points, and will calculate a weighted average of the target value.
