The Basic Problem: Understanding Function Approximation

*

  • function approximation problem target, label, and outcome predictors, regressors, features, and attributes
  • feature engineering Determining what attributes to use for making predictions Data cleaning and feature engineering take 80 percent to 90 percent of a data scientist’s time

Working with Training Data

x_i (with a single index) will refer to the ith row of X

  • x 2 would be a row vector containing the values F, 250, 32.
  • JB: Index startet in Python mit 0, aber bei Bowles mit 1

The targets corresponding to each row in X are arranged in a column vector Y

Assessing Performance of Predictive Models 76

"""regression problem ... describe the error as being the numeric difference between them

mean squared error (MSE)
mean absolute error (MAE)

"""classification problem

misclassification error