What does decrease accuracy mean?
The Mean Decrease Accuracy plot expresses how much accuracy the model losses by excluding each variable. The more the accuracy suffers, the more important the variable is for the successful classification. The variables are presented from descending importance.
Should Gini be high or low?
The Gini index is a measure of the distribution of income across a population. A higher Gini index indicates greater inequality, with high-income individuals receiving much larger percentages of the total income of the population.
What is mean decrease accuracy in random forest?
Mean Decrease in Gini is the average (mean) of a variable’s total decrease in node impurity, weighted by the proportion of samples reaching that node in each individual decision tree in the random forest. A higher Mean Decrease in Gini indicates higher variable importance. Beside above, what is Node purity in random forest?
How is the feature importance of the random forest computed?
The Random Forest algorithm has built-in feature importance which can be computed in two ways: Gini importance (or mean decrease impurity), which is computed from the Random Forest structure. Let’s look how the Random Forest is constructed. It is a set of Decision Trees.
How does mtry affect the strength of a random forest?
The strength of each individual tree in the forest. A tree with a low error rate is a strong classifier. Increasing the strength of the individual trees decreases the forest error rate. Reducing mtry ( Number of random variables used in each tree) reduces both the correlation and the strength. Increasing it increases both.
How to calculate misclassification rate in random forest?
For each tree, using the leftover (36.8%) data, calculate the misclassification rate – out of bag (OOB) error rate. Aggregate error from all trees to determine overall OOB error rate for the classification. If we grow 200 trees then on average a record will be OOB for about .37*200=74 trees.