Hands-On Machine Learning with R

Chapter 1.1 Supervised Learning - Summary

Predictive modeling: the process of developing a mathematical tool or model that generates an accurate prediction

The learning algorithm in a predictive model attempts to discover and model the relationships among the target variable and the other features.

Example: using production attributes to predict time to market.

use attributes X to predict an outcome measurement Y

  • X: “predictor variable”, “independent variable”, “attribute”, “feature”, “predictor”

  • Y: “target variable”, “dependent variable”, “response”, “outcome measurement”.

Supervising learning: The supervision refers to the fact that the target values provide a supervisory role, which indicates to the learner the task it needs to learn.

In supervised learning, the training data you feed the algorithm includes the target values. Consequently, the solutions can be used to help supervise the training process to find the optimal algorithm parameters. –> the learning algorithm attempts to optimize a function.

Regression Problems:

Objective: to predict a numeric outcome –> Revolves around predicting output that falls on a continuum.

Example: Figure 1.1 illustrates average home sales prices as a function of two home features: year built and total square footage. Depending on the combination of these two features, the expected home sales price could fall anywhere along a plane

Classification Problems:

Objective: to predict a categorical outcome. –> Revolves around predicting a binary or multinomial response measure.

Example: Classifying customer reviews:

  • Binary: positive vs. negative.

  • Multinomial: extremely negative to extremely positive on a 0–5 Likert scale.

When we apply machine learning models for classification problems, rather than predict a particular class.

There are machine learning algorithms that can be applied to regression problems but not classification and vice versa. There are algorithms that can be applied to both.