What are some best practices or tips for interpreting and communicating the results of random forests?

1 Understand the basics

Before you dive into the details of your random forest model, make sure you understand the basics of how it works and what it does. A random forest is a collection of decision trees that are trained on different subsets of the data and features, and then combined to make predictions. The main advantages of random forests are that they can reduce overfitting, capture non-linear relationships, and handle missing values and outliers. However, the main challenges of random forests are that they can be computationally expensive, prone to multicollinearity, and difficult to explain.

2 Use feature importance

One of the most common and useful ways to interpret and communicate the results of random forests is to use feature importance. Feature importance measures how much each feature contributes to the prediction accuracy of the model. It can help you identify the most relevant and influential features, as well as the potential interactions and correlations among them. You can use different methods to calculate feature importance, such as Gini impurity, permutation, or SHAP values. You can also visualize feature importance using bar plots, histograms, or heatmaps.

3 Explore partial dependence plots

Another way to interpret and communicate the results of random forests is to use partial dependence plots. Partial dependence plots show how the predicted outcome changes as a function of one or two features, while holding all other features constant. They can help you understand the marginal effects and non-linear relationships of the features, as well as the interactions and heterogeneity among them. You can use different packages or libraries to create partial dependence plots, such as pdp, ICEbox, or scikit-learn.

4 Evaluate model performance

Besides interpreting the results of random forests, you also need to evaluate their performance and validity. You can use different metrics and methods to assess how well your random forest model fits the data and generalizes to new data, such as accuracy, precision, recall, ROC curve, AUC, confusion matrix, or cross-validation. You can also compare your random forest model with other models, such as linear regression, logistic regression, or neural networks, to see if it offers any advantages or disadvantages.

5 Consider causal inference

If you are interested in not only predicting the outcome, but also understanding the causal effects of the features, you need to consider causal inference. Causal inference is the process of estimating the impact of an intervention or treatment on an outcome, while accounting for confounding factors and selection bias. Random forests can be used for causal inference, but they require some modifications and assumptions. For example, you can use propensity score matching, inverse probability weighting, or causal trees to estimate the average treatment effect or the conditional average treatment effect.

6 Communicate with clarity

Finally, when you communicate the results of random forests to your audience, you need to do it with clarity and simplicity. You need to tailor your message and format to the level of knowledge and interest of your audience, whether they are technical experts, decision makers, or general public. You need to highlight the main findings and implications of your analysis, while acknowledging the limitations and uncertainties. You need to use appropriate visualizations, narratives, and examples to illustrate your points, while avoiding jargon, acronyms, and technical details.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?