Application Variance Online With Random Forest
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FAQ
To sum up, the Random Forest employs a number of techniques to reduce variance in predictions while maintaining (to some extent) the low variance that was characteristic of the lone Decision Tree. It does this primarily by averaging together a number of very weakly correlated (if not completely uncorrelated) trees.
To understand bias variance in random forest it is important to understand bias variance in decision trees. In a decision tree if the tree is shallow then it might have a high bias i.e underfitting. If the tree is too deep then it will have a high variance i.e overfitting.
2. Random Forest algorithm intuition Randomly select k features from a total of m features where k < m . Among the k features, calculate the node d using the best split point. Split the node into daughter nodes using the best split. Repeat 1 to 3 steps until l number of nodes has been reached.
Coding in Python ? Random Forest Let's import the libraries. ... Import the dataset. ... Putting Feature Variable to X and Target variable to y. ... Train-Test-Split is performed. ... Let's import RandomForestClassifier and fit the data. ... Let's do hyperparameter tuning for Random Forest using GridSearchCV and fit the data.
The following steps will tell you how random forest works: Create Bootstrap Samples: Construct different samples of the dataset with replacements by randomly selecting the rows and columns from the dataset. ... Build Decision Trees: Construct the decision tree on each bootstrap sample as per the hyperparameters.