Random Forest is a machine learning algorithm that is a part of the supervised learning methodology. More commonly, it can also be used for Classification & Regression problems in machine learning. It is based on the ensemble learning process, a technique that combines different classifiers to fix a single issue while improving the model's overall performance. Welcome to this all-encompassing guide that explains the basics of random forest algorithms.
Learn More: DevOps & Cloud Engineering
What Is Random Forests Algorithm?
What is random forest algorithms? The random forest algorithm comprises various decision trees. The algorithm establishes an outcome depending on the decision trees' predictions. It predicts by taking the mean or average output from multiple trees. If the trees' number increases, the outcome's precision automatically increases.
Simply put, the random forest can eradicate the limitations of the decision tree algorithm by reducing the overfitting of the datasets and increasing precision. In addition to this, it also generates predictions without configurations in the packages.
How Does the Random Forest Algorithm Work?
Here's the process of how the random forest algorithm works:
- Selecting random samples from the training set or data
- The algorithm constructs a particular decision tree for training data
- Voting occurs by averaging a decision tree
- Now, it comes to selecting the voted prediction results as the ultimate prediction result
A combination of different models is popular as Ensemble, which uses the following methods:
- Bagging: This creates a completely different training subset from the sample training data with a replacement. The final output will be based on the maximum voting.
- Boosting: It creates sequential models in such a manner that the final model comes with the highest accuracy.
Know More: What is Logistic Regression in Machine Learning
Random Forest Algorithms vs. Other Machine Learning Algorithms
Random forest is a sort of ensemble classifier that uses a decision tree algorithm. It consists of various trees of varying shapes and sizes. It also maintains accuracy even when a greater data proportion is not present. These are the ways in which random forest is different from other ML algorithms.
Feature Importance in Random Forest Algorithm
One can measure the feature's importance with the help of different techniques. Random Forest algorithm is used for implementing feature importance in sci-kit-learn. The model offers a property that can easily be accessed to retrieve importance scores for every input feature.
Advantages of the Random Forest Algorithm in ML
The following are the advantages of the random forest algorithm:
- Flexible to regression and classification problems
- Reduces overfitting in the decision trees, thereby improving accuracy
- Automates the missing values in data
- Works excellently with continuous and categorical values
Applications of the Random Forest Algorithm in Machine Learning
The following are the applications of the random forest algorithm in ML:
- Medical Hub
Medical professionals implement random forest systems in order to diagnose patients. Patients can be diagnosed, considering their medical history.
- Banking and Finance
Even the banking universe utilizes random forest techniques to anticipate a loan applicant's creditworthiness. That allows a financial institution to make a well-defined decision for the loan applicant. In addition, banks also use the algorithm for fraudster detection.
The eCommerce vendors also predict customers' preferences depending on the previous consumption behavior via rainforest algorithms.
- Stock market
The applications of random forests are also evident in the stock market. Financial analysts implement it to recognize stock markets. It enables them to detect and measure the behavior of the stocks.
What Is Regression in Random Forests Algorithm?
Random Forest is the ensemble technique capable of performing classification as well as regression tasks by using various decision trees and one technique known as Bootstrap & Aggregation: bagging.
Unlock the Basics of Linear Regression – Types and Applications Explained here.
When to Avoid Using Random Forests Algorithm?
Here's presenting the times when random forest algorithms aren't ideal:
- Extrapolation: It is not ideal in data extrapolation
- Sparse data: It doesn't produce good results when it comes to sparse data
Python Implementation of Random Forest Algorithm
If you want to learn about the python implementation of random forest algorithm, read on. Here, the dataset "user_data.csv" have been used. By using the dataset, one can easily compare the Random Forest classifier with other models like KNN, Decision tree Classifier, Logistic Regression, SVM, and more.
#Visulaizing the test set result
from matplotlib.colors import ListedColormap
x_set, y_set = x_test, y_test
x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step =0.01),
nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))
mtp.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape),
alpha = 0.75, cmap = ListedColormap(('purple','green' )))
for i, j in enumerate(nm.unique(y_set)):
mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],
c = ListedColormap(('purple', 'green'))(i), label = j)
mtp.title('Random Forest Algorithm(Test set)')
What Are the Challenges of Random Forest Algorithm?
The key challenges of random forest algorithm are mentioned below:
- It is time-consuming: Random forest algorithms handle massive data sets. So, they offer accurate predictions. But their predictions are slow.
- Needs Excessive Resources: Random forests algorithm process multiple data sets, so they need more resources for storage purposes.
- Complex: Lastly, a single decision tree's prediction is easier if you compare it to the random forest.
Learn more: Choosing the Right Machine Learning Model for Your Data
Difference between Decision Tree and Random Forest Algorithm
The following are the key differences between random forest and decision tree:
|These have problems of overfitting when allowed to grow without control
||These are created from data subsets, and the final output depends on average/majority ranking. So there's the problem of overfitting
| One decision tree is faster
||When compared to decision trees, it is slower
| They use a certain set of rules if the data set with features are considered as input.
||Random Forest selects observations and creates one decision tree. After that, the result is gained depending on maximum voting. So, there's no formula included.
So, now you have learned everything about random forest algorithm, their applications, and differencing factors. Check out Hero Vired’s programs in Data Science can help a professional gain a lucrative career ahead. Discover the online program’s basics from the platform.