Gradient Descent is, in essence, a simple optimization algorithm. It seeks to find the gradient of a linear slope, by which the resulting linear line best fits the observed data, resulting in the smallest or lowest error(s). It is THE inner working of the linear functions we get taught in university statistics courses, however, many of us will finish our Masters (business) degree without having heard the term. Hence, this blog.

Linear regression is among the simplest and most frequently used supervised learning algorithms. It reduces observed data to a linear function (Y = a + bX) in order to retrieve a set of general rules, or to predict the Y-values for instances where the outcome is not observed.

One can define various linear functions to model a set of data points (e.g. below). However, each of these may fit the data better or worse than the others. How can you determine which function fits the data best? Which function is an optimal representation of the data? Enter stage Gradient Descent. By iteratively testing values for the intersect (a; where the linear line intersects with the Y-axis (X = 0)) and the gradient (b; the slope of the line; the difference in Y when X increases with 1) and comparing the resulting predictions against the actual data, Gradient Descent finds the optimal values for the intersect and the slope. These optimal values can be found because they result in the smallest difference between the predicted values and the actual data – the least error.

Afbeeldingsresultaat voor linear regression plot r

The video below is part of a Coursera machine learning course of Stanford University and it provides a very intuitive explanation of the algorithm and its workings:

A recent blog demonstrates how one could program the gradient descent algorithm in R for him-/herself. Indeed, the code copied below provides the same results as the linear modelling function in R’s base environment.

gradientDesc  max_iter) { 
      abline(c, m) 
      converged = T
      return(paste("Optimal intercept:", c, "Optimal slope:", m))
    }
  }
}

# compare resulting coefficients
coef(lm(mpg ~ disp, data = mtcars)
gradientDesc(x = disp, y = mpg, learn_rate = 0.0000293, conv_theshold = 0.001, n = 32, max_iter = 2500000)

Although the algorithm may result in a so-called “local optimum”, representing the best fitting set of values (a & b) among a specific range of X-values, such issues can be handled but deserve a separate discussion.