# Points Classifier *English translation by SOTA – AI Community of the Romanian original, published with the permission of Asociația Nitro. Organisers who would like this translation removed can email team@sota-ai.org.* ## Problem 2 In this problem you will work with data representing points in the plane, specified by their coordinates (X,Y) with 0 <= X,Y <= 100. In the same region of the plane, [0, 100] x [0, 100], there is a rectangle with sides parallel to the axes, but whose coordinates are not known. Any point in the region [0, 100] x [0, 100] will be classified as positive (label 1) if it lies inside this rectangle (including on its sides) and as negative (label 0) if it lies outside that rectangle. *[Figure: see the [original statement](https://docs.google.com/document/d/19fE3_9nExRl5aR6fTGmZzZt1wjb2Lovd/edit). It shows the square region with an axis-parallel rectangle inside it; the points inside the rectangle are marked "+" and the points outside it are marked "−".]* Your task is to start from a set of training examples (points labelled 0 or 1) and build a classifier that labels any point in the region [0, 100] x [0, 100] as positive (label 1) or negative (label 0) according to the mechanism described above. The performance of the classifier will be given by its accuracy on a set of test examples. You will have to investigate three scenarios / situations, which will differ in the number of training examples. Each of the three scenarios will be a problem on Kaggle. The three scenarios have nothing in common except the mechanism described above, and the rectangle that performs the classification will not necessarily be the same. You can find the technical details about the format of the training, test and solution files on Kaggle: Scenario 1 (100 training examples, 5000 test examples): Scenario 2 (1000 training examples, 5000 test examples): Scenario 3 (5000 training examples, 5000 test examples):