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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 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.
Here's the process of how the random forest algorithm works:
A combination of different models is popular as Ensemble, which uses the following methods:
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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.
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.
The following are the advantages of the random forest algorithm:
The following are the applications of the random forest algorithm in ML:
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.
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Here's presenting the times when random forest algorithms aren't ideal:
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.
Note that the classifier object implements the following parameters:
#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.xlabel('Age') mtp.ylabel('Estimated Salary') mtp.legend() mtp.show()
The key challenges of random forest algorithm are mentioned below:
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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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