| Figure 11.1 - How a decision tree learns rules from data.png | 44 KB | |
| Figure 11.10 - Feature importance for the best regression and classification models.png | 169.5 KB | |
| Figure 11.11 - Bias-variance breakdown for individual and bagged decision trees.png | 954.1 KB | |
| Figure 11.12 - How a random forest grows individual trees.png | 38.5 KB | |
| Figure 11.13 - Random forest feature importance.png | 121.9 KB | |
| Figure 11.14 - Distribution of the daily mean information coefficient for various model configurations.png | 66.9 KB | |
| Figure 11.15 - OLS coefficients and confidence intervals for the various random forest configuration parameters.png | 139.7 KB | |
| Figure 11.16 - Alphalens factor signal evaluation.png | 87.9 KB | |
| Figure 11.17 - Pyfolio strategy evaluation.png | 258.6 KB | |
| Figure 11.2 - Mutual information for features and returns or price move direction.png | 213 KB | |
| Figure 11.3 - OLS results and regression tree.png | 95.6 KB | |
| Figure 11.4 - Decision surfaces for linear regression and the regression tree.png | 17.4 KB | |
| Figure 11.5 - Classification loss functions.png | 131.6 KB | |
| Figure 11.6 - Visualization of a classification tree.png | 215.3 KB | |
| Figure 11.7 - Visualization of a classification tree.png | 162.1 KB | |
| Figure 11.8 - Train and validation scores for both models.png | 52.3 KB | |
| Figure 11.9 - Learning curves for the best version of each model.png | 244.7 KB | |
