Random Forest Algorithms in Machine Learning: A Comprehensive study
When you address computational problems, it becomes inevitable to compute it in a very authentical way, we simply can’t rely on mild algorithmic and generalized approaches. Instead, we need such algorithms that have higher computational capabilities and exert fewer cost and reduce process timing.
Machine learning is the trendiest technology in today’s fashion of technology, it is deemed as a subfield of Artificial Intelligence and addressed with the advancement of techniques and methods to make the computer learn.
It entails estimating and analyzing data such that we deploy various machine learning algorithms with respect to the dataset and problem statement. ML algorithms are modeled in a manner that enables machines to acquire and execute several tasks and activities.
With the passing of time, many techniques and methodologies are promoted for machine learning and deep learning tasks including data classifications.
This blog will introduce the same concept. One well-defined approach is Random Forest, during this article, you will gain insight into Random Forest, its vital practice, its featuring qualities, the role of bagging and boosting and why it is used over decision tree algorithms.
Understanding Random Forest
Before understanding random forest you must have the prerequisite of Decision Trees, you can read my previous blog to acquire decent inference to Decision Trees. In the nutshell, decision trees are the building blocks for Random Forest, it is a predictive modeling technique that acts as a decision-making approach.
It can be represented in a tree-based structure or design to get accurate inferences. It works on a model that foretells the estimated value of a dependent variable while considering another variable into the model to make decisions respectively.
Now let’s move to our core concept: Random Forest
Random Forest is the most versatile machine learning approach in today’s world, having inbuilt ensembling capacity that is designing a generalized model more decently.
Similar to Decision-tree, Random Forest is a tree-based algorithm (model) comprised of several decision trees, merging their output to enhance the performance of a model where the mode of merging trees is termed as an Ensemble Method. Ensembling is basically looking for a strong practice when making individual weak practice(trees) together.
In Layman terms, the random forest consolidates thousands of decision trees, trains each tree on a separate set of observations, divides nodes in each tree assuming a limited number of attributes or features. The final outcome of the random forest is secured by making an average of the predictions of each individual tree.
As a real-life analogy, suppose you want to buy a product and are uncertain in its reviews, now if you ask 5 people about how the product is, 4 of them said,” Product is nice”, and one denies, it is highly likely you buy the product as the majority is in favor. The likewise is an Ensemble technique in our regular time.
There are some fundamental terms, with the help of random forest algorithms make decisions,
- Entropy: It is a degree or strength of randomness or unpredictability (changeability) in a given dataset.
- Information gain: When the dataset gets split, there is a reduction in entropy and that decreased-measurement is called information gain.
- Leaf-nodes: It is particularly a node or intersection to support the classification or decisions.
- Decision-node: A simple node that has two or more branches or intersections.
- Root-node: The leading or uppermost decision node, where all the data is available.
How does it work?
We can surmise the functioning of random forest algorithms in the subsequent easy runs; originating with picking random samples from the available dataset, the next random forest algorithm creates a decision tree for each and every sample and obtains the estimated results from each decision tree. Then, voting of estimated results will be conducted. In the last step, the most voted prediction result is acknowledged as the terminal estimated result.
The consequent picture illustrates its working −
In addition to that, while making a fusion of decision trees, there are two ways to consider; Bagging also called Bootstrap Aggregation(used in the random forest) and Boosting (used in the gradient boosting machines). I am giving a small brief about their work.
Bagging is the default method employed with Random Forest such that each decision tree is trained on the random subsets of data samples where sampling is done with replacement.
Bagging is there to diminish the variance of the model. Every tree is likely to overfit data and sensitive to data noise. Until the trees are not correlated fusing them with bagging will make them more hale without raising bias.
Random Forest also accepts features bagging, it selects only a random subset of important features at each split. It reduces correlation and the impact of the very strong predictor entity.
“The key takeaway is that correlation is an understandable equation that relates the amount of change in x and y. If the two variables have a consistent change, there will be a high correlation; otherwise, there will have a lower correlation. ” ― Scott Hartshorn,
Somewhere Boosting works similarly but the difference is, samples are weighted for sampling first such that incorrectly predicted samples got bigger weight and so sampled more often. Due to this major difference, bagging can be readily paralleled or correlated whereas boosting executed sequentially.
Random Forest is used to overcoming the limitations of Decision Trees
The very fundamental difference between Random Forest and Decision Trees algorithms is the method to identify the root modes and splitting the traits nodes executes randomly.
While running the dataset over the Decision Tree algorithm, there appears an issue of overfitting due to which result-making went worse, instead, the random forest has enough decision trees in the forest that don’t let classifier to overfit the model.
“The higher the number of trees under the shed of forest traverses greater accuracy and counters the obstacle of overfitting.“
More looking for advanced traits between them, the random forest can oversee missing values and can be shaped for categorical values.
Random Forest: Essentials
- Random Forest is applicable to both regression and classification problems with the difference that the dependent variable is continuous and categorical to regression and classification respectively.
- It is unrivaled amidst the prevailing set of algorithms and executes more skillfully on huge datasets, even can handle multiple input variables at a time without crossing out other variables.
- It can deliver an approx estimation of important variables in classificaton and propose experimental procedure to figure out variable interactions.
- It calculates concurrences between a set of cases that are used for detecting outliers, clustering, or scaling to datasets.
- It has the capability to maintain accuracy while a huge portion of data is missing in the available dataset.
- In some cases, models are executed to obtain data that reveal the links between variables and classification, it can be stretched to unlabeled data or unsupervised clustering, data aspects and outlier apprehensions as well.
Conclusion
In the Random Forest, every tree endeavors to formulate rules such that each resultant node should be pure up to a possible extent. Higher the purity, the higher the chance to make certain decisions.
The typical random forest algorithms strategies used in Banking, to find loyal and fraud customers, in medicine, to figure out the worth component and their weight in medicines, to find out the disease by investigating patient’s medical reports, in e-commerce, to predict the likeliness of the recommended products by customer’s reviews, and many more.
I believe this blog gives you enough information about the random forest and its features, which motivates you to read and search more about machine learning.
