Finding points in 3D space - SVM

Question

In the SVM classification, we use planes to classify the labels points if the dataset has 3 input features. We need to use planes when input features are 3. I am describing a toy dataset with 3 input features as follows

Study_time    rest_time   pass_time    label
40            10          5            Good
38            12          3            Good
20            8           10           bad
15            12          2            bad

In this dataset you can see, three input features are study_time, rest_time, pass_time. We need to define a plane to find out the label. I went through various course materials of support vector machines and every material said that we need to define points in 3D space if the number of features is 3.

I know points mean a dot which has an x co-ordinate and a y-co-ordinate.

In toy dataset, every instance has 3 values, 1 for study times, 1 for rest_time, 1 for pass_time. 1st instance can be defined as (40, 10, 5).

If we consider the points with respect to the toy dataset, which are those points? what are there co-ordinates?

Thank you.

Answer 1

By points they mean x,y and z components. Not just an x and a y. In the same way that you need two points to uniquely define a line, you need 3 "points" to define a plane. The points in the dataset are simply the numerical values for each row: (40,10,5),..., (15,12,2).