Task 2: Machine Learning automatic resolution

Learning methods require information to be structured into elements/observations/cases/instances, and each observation must have characteristics/indicators/properties that are measured. You want to know the classification (or the estimation of an indicator) of an element based on its features. The variable you want to know can be binary, discrete or real. We will focus our examples on binary variables, as this is the purest case of learning. For the sake of ease and affinity to statistical methods, we can call independent variables the characteristics/indicators/properties, the dependent variable what we want to know, and observations the elements/observations/cases/instances.

For supervised learning methods to work, there must be past observations with classifications, so all the independent variables and the value of the dependent variable must be known, and a given method can be trained to classify unseen observations. This information can be in the form of an array, with the independent variables in columns and the observations in rows.

To load into R, we can place the data in a .csv file and read it (data.csv):

Action:	Expected:
> setwd("set working folder") > data = read.csv("data.csv", header = TRUE, sep=";") > data

This is an example data file with student achievements in a given class. The columns in this file are as follows: Case; Materials; FAs; Interventions; Evaluations; Grade. The first column only has the case ID, which is not relevant, and the last column is the dependent variable with the grade. All the others are the independent variables from which we want to predict the student's grade.

Action:	Expected:
> data[,2:5] > data[,6]

As we can see, extraction of independent and dependent variables is easy, from a .csv formatted file.