IGNOU MCS 67 2026 2027 - (a) Explain different methods of visualising random forests.

Question

(a) Explain different methods of visualising random forests.
(b) Explain the use of the bivariate histogram and kernel density estimation with the help of a suitable
example.
(c) Explain the Trellis Paradigm. Why is it needed?
(d) Explain various regression visualisations with the help of an example

30 Jul 2026

Answer :

Word Count : 654

Random forests are ensemble learning models that combine multiple decision trees to improve prediction accuracy and reduce overfitting. Since random forests contain many trees and complex relationships among variables, visualization techniques are used to understand model structure, feature importance, decision patterns, and prediction behaviour. Different methods of visualising random forests include tree visualization, feature importance plots, partial dependence plots, variable interaction plots, and error analysis graphs. Tree visualization represents individual decision trees within the forest using nodes and branches. It helps in understanding how the model makes decisions based on different conditions. Feature importance plots display the contribution of each input variable in making predictions. Variables with higher importance values have a greater influence on the model output. Partial dependence plots show the relationship between a particular feature and the predicted outcome while keeping other variables constant. They help in understanding the effect of individual predictors on model behaviour. Variable interaction plots are used to examine how

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