Random Forest

Decision trees are good classifier but have high tendency for overfitting as the complexity of the trees grows. This can be avoided by not depending on single tree rather growing multiple trees on randomly sampled data (preferably bootstrap sampling) and making a decision by vote. Issue may arise when there is a strong predictor present in your dataset which would results all the trees with same root. Hence better to have sampling for features too and grow multiple tree with different roots giving an appearance of forest of randomly grown trees of heterogenous population known as Random Forest. 

Introduction

Random forests are a popular ensemble learning method for classification and regression problems. They are known for their ability to reduce overfitting and improve the overall performance of the model. It is an ensemble method, which means that it combines the predictions of multiple decision trees to make a more accurate final prediction. In this blog post, we will dive into the details of how random forests work and the various benefits they offer.

How do Random Forests work?

The basic building block of a random forest is a decision tree. A decision tree is a simple model that can be used for both classification and regression problems. The tree is constructed by recursively splitting the data based on the values of the input features. At each split, the tree selects the feature and threshold that result in the best separation of the data. The process continues until a stopping criterion is met, such as a maximum tree depth or a minimum number of samples per leaf. Here's an overview of the steps involved in building a Random Forest:

1. Select a subset of the data by bootstrap sampling to use as the training set for each decision tree.
2. For each decision tree, randomly select a subset of the features to use as the split criteria at each node. This is known as random subspace method.
3. Train a decision tree on each bootstrap sample, using the selected features as the split criteria.
4. For new input data, make a prediction using each decision tree.
5. Combine the predictions of all the decision trees by averaging them (for regression) or by taking the majority vote (for classification).

When training a random forest, the first step is to generate many random subsets of the data. These subsets, also known as bootstrap samples, are used to train the individual decision trees in the forest. The bootstrap samples are typically generated by randomly selecting samples from the original data with replacement. This means that some samples may be selected multiple times, while others may not be selected at all. After generating the bootstrap samples, a decision tree is trained on each one. During the training process, a random subset of the features is selected at each split to increase the diversity of the trees. This is known as feature bagging or random subspace method. The final model is the average of all trees predictions.

Advantages of Random Forests

One of the main advantages of random forests is their ability to handle large numbers of input features. Because the trees are trained on different subsets of the data and features, they are able to capture different patterns in the data and therefore are less likely to overfit. Random forests are also very effective at dealing with noisy or missing data.
Random forests can also be used for feature importance analysis. One way to measure feature importance is to calculate the average reduction in impurity of the split from each feature. This gives you a sense of how much a feature contributes to the overall decision making process.
Another important feature of random forests is their ability to handle categorical variables. Unlike other decision tree based models, random forests can work with categorical variables directly, without the need for one-hot encoding or other similar methods.

Conclusion

In conclusion, random forests are a powerful ensemble learning method that can be used for both classification and regression problems. By combining the predictions of multiple decision trees, they are able to reduce overfitting and improve the overall performance of the model. They are also effective at dealing with large numbers of input features, noisy or missing data and handling categorical variables. Understanding how random forests work, and taking advantage of the various benefits they offer can greatly improve the performance of your machine learning models.

"The one solution to your prediction problems is the countless trees of Random Forest"