Machine Learning for Absolute Beginners: A Plain English Introduction (First Edition)

your company may wish to examine a subset of customers that purchase at the same time of year and discern what factors influence their purchasing behavior.

You will know if your model is accurate when the error rate between the training and test dataset is low. This means that the model has learned and understood the underlying patterns and trends found in the data.

A major advantage of unsupervised learning is that it enables you to discover patterns in the data that you weren’t aware existed—such as the presence of two genders.

conceded). The data is therefore already pre-tagged, and we know the final outcome of the existing data. Each previous game has a final outcome in the form of the match score.

One variable is the result you wish to predict, known as the dependent variable (y).

One helpful approach to analyzing data is to identify clusters of data that share similar attributes.

While it will depend on your exact dataset, 100-150 decision trees is often a recommended starting point.

Logistic regression is typically used for binary classification to predict two discrete classes, i.e. pregnant or not pregnant.

An example of this could be a creating a model that detects spam email messages. The model is trained to block emails with suspicious subject lines and body text containing three or more flagged keywords: dear friend, free, invoice, PayPal, Viagra, casino, payment, bankruptcy, and