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Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging.
In this post you will discover the Bagging ensemble algorithm and the Random Forest algorithm for predictive modeling. After reading this post you will know about:
- The bootstrap method for estimating statistical quantities from samples.
- The Bootstrap Aggregation algorithm for creating multiple different models from a single training dataset.
- The Random Forest algorithm that makes a small tweak to Bagging and results in a very powerful classifier.
This post was written for developers and assumes no background in statistics or mathematics. The post focuses on how the algorithm works and how to use it for predictive modeling problems.
If you have any questions, leave a comment and I will do my best to answer.
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Before we get to Bagging, let’s take a quick look at an important foundation technique called the bootstrap.
The bootstrap is a powerful statistical method for estimating a quantity from a data sample. This is easiest to understand if the quantity is a descriptive statistic such as a mean or a standard deviation.
Let’s assume we have a sample of 100 values (x) and we’d like to get an estimate of the mean of the sample.
We can calculate the mean directly from the sample as:
mean(x) = 1/100 * sum(x)
We know that our sample is small and that our mean has error in it. We can improve the estimate of our mean using the bootstrap procedure:
- Create many (e.g. 1000) random sub-samples of our dataset with replacement (meaning we can select the same value multiple times).
- Calculate the mean of each sub-sample.
- Calculate the average of all of our collected means and use that as our estimated mean for the data.
For example, let’s say we used 3 resamples and got the mean values 2.3, 4.5 and 3.3. Taking the average of these we could take the estimated mean of the data to be 3.367.
This process can be used to estimate other quantities like the standard deviation and even quantities used in machine learning algorithms, like learned coefficients.
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Bootstrap Aggregation (Bagging)
Bootstrap Aggregation (or Bagging for short), is a simple and very powerful ensemble method.
An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model.
Bootstrap Aggregation is a general procedure that can be used to reduce the variance for those algorithm that have high variance. An algorithm that has high variance are decision trees, like classification and regression trees (CART).
Decision trees are sensitive to the specific data on which they are trained. If the training data is changed (e.g. a tree is trained on a subset of the training data) the resulting decision tree can be quite different and in turn the predictions can be quite different.
Bagging is the application of the Bootstrap procedure to a high-variance machine learning algorithm, typically decision trees.
Let’s assume we have a sample dataset of 1000 instances (x) and we are using the CART algorithm. Bagging of the CART algorithm would work as follows.
- Create many (e.g. 100) random sub-samples of our dataset with replacement.
- Train a CART model on each sample.
- Given a new dataset, calculate the average prediction from each model.
For example, if we had 5 bagged decision trees that made the following class predictions for a in input sample: blue, blue, red, blue and red, we would take the most frequent class and predict blue.
When bagging with decision trees, we are less concerned about individual trees overfitting the training data. For this reason and for efficiency, the individual decision trees are grown deep (e.g. few training samples at each leaf-node of the tree) and the trees are not pruned. These trees will have both high variance and low bias. These are important characterize of sub-models when combining predictions using bagging.
The only parameters when bagging decision trees is the number of samples and hence the number of trees to include. This can be chosen by increasing the number of trees on run after run until the accuracy begins to stop showing improvement (e.g. on a cross validation test harness). Very large numbers of models may take a long time to prepare, but will not overfit the training data.
Just like the decision trees themselves, Bagging can be used for classification and regression problems.
Random Forests are an improvement over bagged decision trees.
A problem with decision trees like CART is that they are greedy. They choose which variable to split on using a greedy algorithm that minimizes error. As such, even with Bagging, the decision trees can have a lot of structural similarities and in turn have high correlation in their predictions.
Combining predictions from multiple models in ensembles works better if the predictions from the sub-models are uncorrelated or at best weakly correlated.
Random forest changes the algorithm for the way that the sub-trees are learned so that the resulting predictions from all of the subtrees have less correlation.
It is a simple tweak. In CART, when selecting a split point, the learning algorithm is allowed to look through all variables and all variable values in order to select the most optimal split-point. The random forest algorithm changes this procedure so that the learning algorithm is limited to a random sample of features of which to search.
The number of features that can be searched at each split point (m) must be specified as a parameter to the algorithm. You can try different values and tune it using cross validation.
- For classification a good default is: m = sqrt(p)
- For regression a good default is: m = p/3
Where m is the number of randomly selected features that can be searched at a split point and p is the number of input variables. For example, if a dataset had 25 input variables for a classification problem, then:
- m = sqrt(25)
- m = 5
For each bootstrap sample taken from the training data, there will be samples left behind that were not included. These samples are called Out-Of-Bag samples or OOB.
The performance of each model on its left out samples when averaged can provide an estimated accuracy of the bagged models. This estimated performance is often called the OOB estimate of performance.
These performance measures are reliable test error estimate and correlate well with cross validation estimates.
As the Bagged decision trees are constructed, we can calculate how much the error function drops for a variable at each split point.
In regression problems this may be the drop in sum squared error and in classification this might be the Gini score.
These drops in error can be averaged across all decision trees and output to provide an estimate of the importance of each input variable. The greater the drop when the variable was chosen, the greater the importance.
These outputs can help identify subsets of input variables that may be most or least relevant to the problem and suggest at possible feature selection experiments you could perform where some features are removed from the dataset.
Bagging is a simple technique that is covered in most introductory machine learning texts. Some examples are listed below.
- An Introduction to Statistical Learning: with Applications in R, Chapter 8.
- Applied Predictive Modeling, Chapter 8 and Chapter 14.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Chapter 15
In this post you discovered the Bagging ensemble machine learning algorithm and the popular variation called Random Forest. You learned:
- How to estimate statistical quantities from a data sample.
- How to combine the predictions from multiple high-variance models using bagging.
- How to tweak the construction of decision trees when bagging to de-correlate their predictions, a technique called Random Forests.
Do you have any questions about this post or the Bagging or Random Forest Ensemble algorithms?
Leave a comment and ask your question and I will do my best to answer it.