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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 _______ ________ ______ ____ ______ _____.
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