Random Forest is one of the most powerful and widely used machine learning algorithms in both academia and industry. As the name suggests, it is literally a “forest” made up of many individual “decision trees.” Just like a forest is stronger and more resilient than a single tree, Random Forest leverages the combined strength of multiple decision trees to create a model that is more accurate, robust, and resistant to overfitting. To understand Random Forest, we should first recall how a decision tree works. A decision tree is a flowchart-like structure where data is split into branches based on certain conditions. It is easy to interpret, but the major drawback is that a single tree often becomes too specific to the training data, leading to overfitting and poor performance on unseen data. Random Forest solves this problem by building many trees and combining their outputs, making the overall model generalize much better. The “randomness” in Random Forest comes from two main ideas: Random Sampling of Data (Bagging): Each tree is trained on a random subset of the dataset. This is done using bootstrapping, where we sample data with replacement. As a result, every tree sees a slightly different version of the dataset. Random Selection of Features: When splitting a node, instead of considering all features, Random Forest chooses a random subset of features. This ensures that not all trees look alike and promotes diversity in the forest. When it comes to making predictions, Random Forest works through majority voting in classification tasks and averaging in regression tasks. For example, if we are classifying whether an email is spam or not, each tree gives its vote, and the final decision is based on the majority. If we are predicting house prices, each tree outputs a predicted value, and the Random Forest takes the average. The advantages of Random Forest are numerous. It handles large datasets with higher dimensionality very effectively, works well with both categorical and numerical features, and is robust against noise and missing values. Unlike linear models, it does not assume any specific distribution of data and can capture non-linear relationships very naturally. In real-world scenarios, Random Forest is used in a wide range of applications. In finance, it helps in credit scoring and fraud detection. In healthcare, it is used for disease prediction and medical image classification. In e-commerce, it powers recommendation systems and customer segmentation. Because of its flexibility, Random Forest is often the first algorithm data scientists try when faced with a new problem. However, Random Forest is not without limitations. It requires more computational power and memory compared to a single decision tree. The model can also become less interpretable as the number of trees increases, since it is harder to explain hundreds of trees compared to one. Still, techniques like feature importance ranking allow us to gain insights into which

1. RANDOM
FOREST

2. Agenda
• What Is Random Forest And History
• What is Supervised Learning
• What Is Decision Tree
• Decision Tree Important Terms
• How Does Decision Tree Works
• Ensemble Learning
• Dataset Preparation
• Bagging At Training Time
• Bagging At Inference Time
• Random Subspace Method At Training Time
• Random Subspace Method At Inference Time
• Definition
• Random Forest Model
• Why Use Random Forest Model
• Advantages & Disadvantages
• Random Forest Application
R A N D O M F O R E S T 2

3. Introduction
• A random Forest Algorithm is a supervised machine learning Algorithm consisting
decision trees.
• The general method of random decision forests was first proposed by Ho in 1995.
After that, It was developed by Leo Breiman in 2001.
R A N D O M F O R E S T 3

4. 4
What Is Supervised Learning:
1. Supervised learning is the type of machine learning in which machines are trained using
labeled training data.
2. On the basis of that data, machines predict the output.
3. The labeled data means some input data is already tagged with the correct output.
R A N D O M F O R E S T

5. Decision Tree
R A N D O M F O R E S T 5

6. Decision Tree – Important Terms
R A N D O M F O R E S T 6

7. 7
How Decision Tree Works
• It follows a tree-like model of decisions and their possible consequences.
• The algorithm works by recursively splitting the data into subsets based on the most significant feature at
each node of the tree.
R A N D O M F O R E S T

8. How Decision Tree Works
R A N D O M F O R E S T 8

9. 9
Ensemble Learning
Ensemble learning creates a stronger model by aggregating the predictions of multiple weak models. Random
Forest is an example of ensemble learning where each model is a decision tree. The idea behind it is- the
wisdom of the crowd. The majority vote aggregation can have better accuracy than the individual models.
R A N D O M F O R E S T

10. Dataset
Preparation
Bootstrap Aggregating (Bagging)
Creating a different training subset randomly from the
original training dataset with replacement is called
Bagging. With replacement refers to the bootstrap sample
having duplicate elements. Reduces variance, helps to
avoid overfitting.
Out Of Bag (OOB)
The out-of-bag dataset represents the remaining elements
which were not in the bootstrap dataset. Used for cross
validation or to evaluate the performance.
Random Subspace Method(Feature Bagging)
While building the tree, for splitting, a randomly selected
subset of the features are used. So, the trees are
more different having low correlation.
R A N D O M F O R E S T 10

11. Bagging at training time​
R A N D O M F O R E S T 11

12. Bagging at inference time
R A N D O M F O R E S T 12

13. Random Subspace Method at training time
R A N D O M F O R E S T 13

14. Random Subspace Method at inference time
R A N D O M F O R E S T 14

15. 15
Definition
The Random Forest algorithm is an ensemble learning method consisting of many decision
trees that are built by using bagging and feature bagging which helps to create an
uncorrelated forest of trees whose combined prediction is more accurate than that of a single
tree. For classification tasks, final prediction is done by taking majority votes and for
regression tasks, average of all the individual trees.
R A N D O M F O R E S T

16. Random Forest Model
R A N D O M F O R E S T 16

17. 17
Why Use Random Forest
 Random forests are an effective tool in prediction.
 Forests give results competitive with boosting and adaptive bagging, yet do not
progressively change the training set.
 Random inputs and random features produce good results in classification- less so in
regression.
 For larger data sets, we can gain accuracy by combining random features with boosting.
R A N D O M F O R E S T

18. Advantages
 Versatile uses
 Easy-to-understand hyperparameters
 Classifier doesn't overfit with enough
trees
Disadvantages
 Increased accuracy requires more trees
 More trees slow down model
 Can’t describe relationships within data
Advantages and Disadvantages

19. 19
Random Forest Applications
 Detects reliable debtors and potential fraudsters in finance
 Verifies medicine components and patient data in healthcare
 Gauges whether customers will like products in e-commerce
R A N D O M F O R E S T

20. Thank you
R A N D O M F O R E S T 20

21. 21
Resources
• https://www.simplilearn.com/tutorials/machine-learning-tutorial/random-forest-algorithm#GoTop
• https://en.wikipedia.org/wiki/Bootstrap_aggregating
• https://en.wikipedia.org/wiki/Random_subspace_method
• https://en.wikipedia.org/wiki/Random_forest
• https://mlu-explain.github.io/random-forest/
• https://docs.google.com/presentation/d/1Yu479Jz31rKr-I9dRI6YO_8xcd6bRvKpU_B9sg9ozvY/edit#slide=id.p16
• https://developers.google.com/machine-learning/decision-forests/random-forests
• https://towardsdatascience.com/demystifying-the-random-forest-8a46f4fd416f
• https://www.ibm.com/topics/random-forest
• https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/
R A N D O M F O R E S T