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?