Decision trees – learning rules from data
A decision tree is a machine learning algorithm that predicts the value of a target variable based on decision rules learned from data. The algorithm can be applied to both regression and classification problems by changing the objective function that governs how the tree learns the rules.
We will discuss how decision trees use rules to make predictions, how to train them to predict (continuous) returns as well as (categorical) directions of price movements, and how to interpret, visualize, and tune them effectively. See Rokach and Maimon (2008) and Hastie, Tibshirani, and Friedman (2009) for additional details and further background information.
How trees learn and apply decision rules
The linear models we studied in Chapter 7, Linear Models – From Risk Factors to Return Forecasts, and Chapter 9, Time-Series Models for Volatility Forecasts and Statistical Arbitrage, learn a set of parameters to predict the outcome...
