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Random forest vs neural network8/20/2023 For this, we will use the same dataset "user_data.csv", which we have used in previous classification models. Now we will implement the Random Forest Algorithm tree using Python. Python Implementation of Random Forest Algorithm Although random forest can be used for both classification and regression tasks, it is not more suitable for Regression tasks.It enhances the accuracy of the model and prevents the overfitting issue.It is capable of handling large datasets with high dimensionality.Random Forest is capable of performing both Classification and Regression tasks.Marketing: Marketing trends can be identified using this algorithm.Land Use: We can identify the areas of similar land use by this algorithm.Medicine: With the help of this algorithm, disease trends and risks of the disease can be identified.Banking: Banking sector mostly uses this algorithm for the identification of loan risk.There are mainly four sectors where Random forest mostly used: Consider the below image: Applications of Random Forest During the training phase, each decision tree produces a prediction result, and when a new data point occurs, then based on the majority of results, the Random Forest classifier predicts the final decision. The dataset is divided into subsets and given to each decision tree. So, this dataset is given to the Random forest classifier. The working of the algorithm can be better understood by the below example:Įxample: Suppose there is a dataset that contains multiple fruit images. Step-5: For new data points, find the predictions of each decision tree, and assign the new data points to the category that wins the majority votes. Step-3: Choose the number N for decision trees that you want to build. Step-2: Build the decision trees associated with the selected data points (Subsets). Step-1: Select random K data points from the training set. The Working process can be explained in the below steps and diagram: Random Forest works in two-phase first is to create the random forest by combining N decision tree, and second is to make predictions for each tree created in the first phase. It can also maintain accuracy when a large proportion of data is missing.It predicts output with high accuracy, even for the large dataset it runs efficiently. ![]()
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