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Random Forest Algorithm

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.


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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:


  1. 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. 
  2. 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.
  • eCommerce
    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.


  • Data Pre-Processing Step:
    First, the data pre-processing occurs with the help of a code.
  • Now coming to fitting its algorithm to a training set:
    Now, you need to fit the algorithm to a training set. To do so, you need to import RandomForestClassifier from sklearn.ensemble library using a coding:Note that the classifier object implements the following parameters:

    1.       n_estimators: These are the number of trees, where 10 is the default value
    2.       criterion: The function used for analyzing the split’s accuracy
  • Predict the Test result
    The model is well-fitted to a training set. Thus, you need to predict the test result. For this reason, you need to create a prediction vector y_pred.
  • Creating confusion matrix
    Now is the right time to create the confusion matrix that measures the incorrect and correct predictions with the help of a code:
  • Visualize the training Set results:
    After this, you need to visualize the result for the training set. For this purpose, you need to plot a graph for the classifier. Note that the classifier predicts No or Yes for users who have ‘Not purchased’ or ‘Purchased’ the product (as done in Logistic Regression)
  • Visualize the test set result
    The coding is for visualizing the test set result:

    #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' ))) 
    mtp.xlim(x1.min(), x1.max()) 
    mtp.ylim(x2.min(), x2.max()) 
    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)') 
    mtp.ylabel('Estimated Salary') 


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:


Decision Trees Random Forest
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.



The random forest model includes a subset of the data points alongside features. They are selected for constructing every decision tree. The n random records, while, on the other hand, the m features will be taken from data sets that feature the k number of records. After this, the individual decision trees will be constructed for every sample.
In the random forest algorithm, if the increment is higher in leaf purity, there will be higher importance of its feature. It is performed for every tree and is averaged amongst the trees. Lastly, it is normalized to 1. There, the overall sum of its importance scores that the Random Forest calculates comes down to 1.
The limitation of the random forest is that a large number of trees might make algorithms ineffective and slow for real-time predictions. These types of algorithms are speedy to train; however, they are quite slow to make predictions upon training.
While using the imbalanced dataset, one can oversample minority classes with the help of replacement. It is a technique popularly known as oversampling. Simultaneously, one can delete the rows from classes and match them with minority classes. This technique is termed undersampling.
Random forest might be used on regression tasks or even classification tasks. It can also be used for continuous as well as categorical target variables.

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