Coding Grandmaster · project 41 of 46 · premium, free for now

Classifying by neighbours

Write classify(point, train, k): sort the training examples by distance to the point, take the k closest, and return the label that appears most among them (if tied, the label of the nearest one).

Learn

Not all learning draws lines. k-nearest neighbours classifies a new point by looking at the k closest examples you already know and taking a vote. Distance between two points is the square root of the sum of squared differences. There is no training at all: the examples are the model. Choosing k matters, because k = 1 trusts a single neighbour and a huge k trusts everyone.

Your task

Write classify(point, train, k): sort the training examples by distance to the point, take the k closest, and return the label that appears most among them (if tied, the label of the nearest one).

Stretch challenge

Find a point where k = 1 and k = 5 disagree. Which would you trust?

← The straight-line formula A perceptron learns AND →

Python

Press Run to see what your program prints.

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Predictions and error

Write predict(x, m, b) and mse(xs, ys, m, b). Print the prediction for each size and the mean squared error of the line with the given m and b, rounded to 2 decimal places.

Start →38

Training by searching

Try every m from 0 to 100 in steps of 5 and every b from 0 to 200 in steps of 10 with the same data. Print the best m, the best b and their error rounded to 2 decimal places.

Start →39

Gradient descent

Starting from m = 0, repeat steps times: compute the gradient, then do m = m - lr * gradient. Print the final m rounded to 4 decimal places and the final mean squared error rounded to 4.

Start →40

The straight-line formula

Compute the slope and intercept with these formulas, print them rounded to 3 decimal places, and print the prediction for new_x rounded to 2 places.

Start →42

A perceptron learns AND

Train a perceptron on the AND truth table starting from zero weights, for the number of epochs, with each update using a learning rate of 1. Print the final weights and bias as w1 w2 b, then the four predictions.

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Practise the idea

🧭 Play Pathfinding Visualizer

Draw walls and watch breadth-first search find a shortest grid path.

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FAQ

Classifying by neighbours: common questions

I am stuck. What can I do?

Press Hint for a nudge, read the message next to each goal, and compare your code with the lesson text. If you still need it, Show answer reveals a model solution. Try to type it yourself rather than copy it.

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