What is vector in terms of machine learning
I want to understand what is a vector in terms of machine learning.
I looked into the below 2 links:
https://en.wikipedia.org/wiki/Support_vector_machine https://en.wikipedia.org/wiki/Feature_vector.
I couldn't understand it fully. Can someone explain this in simple words?
Answer
I would think that much of your problem comes because vector is a general term with many uses. In this case, think of it as a list of values or a row in a table. The data structure is a 1-dimensional array; a vector of N elements is an N-dimensional vector, one dimension for each element.
For instance, the input (3.14159, 2.71828, 1.618) is a vector of 3 elements, and could be represented as a point in 3-dimensional space. Your program would declare a 1x3 array (one-dimensional data structure) to hold the three items.
Does this help you visualize the basic input handling? This is not a difficult problem with a Wronkskian transformation matrix -- it's just a change in format and visualization.
The feature vector is simply one row of input. For instance, in the popular machine learning example of housing price prediction, we might have features (table columns) including a house's year of construction, number of bedrooms, area (m^2), and size of garage (auto capacity). This would give input vectors such as
[1988, 4, 200, 2]
[2001, 3, 220, 1]
etc.