How Random Forests work

Today we are going to capture important points about Random Forest Algorithm. In my previous blog , we learnt about Decision Tree algorithm.

Previous blog: https://arpitatechcorner.wordpress.com/2020/08/13/how-does-decision-tree-algorithm-work/

What is Ensemble
An ensemble is a collection of models is used to make predictions instead of individual models.
For an ensemble to work, each model of it should comply with the following conditions:
o Each model should be diverse. This means the individual models make predictions independent of each other.
o Each model should be acceptable. It means each model is at least better than a random model.
A random forest is an ensemble made using a combination of numerous decision trees.
Steps to follow
Random forests are created using a special ensemble method called bagging. Bagging stands for bootstrap aggregation.
Bootstrapping means creating bootstrap samples from a given data set. A bootstrap sample is created by sampling the rows of a given data set uniformly and with replacements.
Create a bootstrap sample from the training set
Now construct a decision tree using the bootstrap sample. While splitting a node of the tree, only consider a random subset of features. Every time a node has to split, a different random subset of features will be considered.
Repeat steps 1 and 2 n times to construct n trees in the forest. Remember, each tree is constructed independently, so it is possible to construct them parallel to each other.
While predicting a test case, each tree predicts individually, and the final prediction is given by the majority vote of all the trees.
Advantages
A random forest is more stable than any single decision tree because the results get averaged out
A random forest is immune to the curse of dimensionality since only a subset of features is used to split a node.
You can parallelise the training of a forest since each tree is constructed independently.
You can calculate the OOB (out-of-bag) error using the training set;
• The OOB provides a good estimate of the performance of a forest on unseen data.
• Hence, there is no need to split the data into training and validation; you can use all of it to train the forest.
• The final OOB error is calculated by calculating the error on each observation and aggregating it.
• It turns out that the OOB error is as good as a cross-validation error.
Time to build a forest
To construct a forest of S trees on a data set that has M features and N observations, the time taken will depend on the following factors
The number of trees: The time is directly proportional to the number of trees. But, this duration can be reduced by creating the trees parallel to each other.
The size of the bootstrap sample: Generally, the size of a bootstrap sample is 30-70% of N. The smaller the size, the faster it takes to create a forest.
The size of the subset of features while splitting a node: Generally, this is taken as √𝑀 in classification and M/3 in regression.

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