Application of Random Forest in Machine Learning

Random forest uses multiple classification trees to classify the input vector. Each tree has a classification result, and finally the classification with the highest number of votes is selected as the final result.

The above is an introduction to random forest. Next, let’s take a look at the workflow of the random forest algorithm.

Step 1: First select a random sample from the data set.

Step 2: For each sample, the algorithm will create a decision tree. Then the prediction results of each decision tree will be obtained.

Step 3: Each expected outcome in this step will be voted on.

Step 4: Finally, select the prediction result with the most votes as the final prediction result.

Principle of Random Forest Algorithm

Advantages of Random Forest Method

• Through averaging Or integrating the output of different decision trees, which solves the problem of overfitting.
• Random forests perform better than individual decision trees for a wide range of data items.
• The random forest algorithm maintains high accuracy even in the absence of large amounts of data.

Characteristics of Random Forest in Machine Learning

• The most accurate algorithm currently available.
• Suitable for huge databases.
• Can handle tens of thousands of input variables without deleting any of them.
• Calculate the importance of several variables in classification.
• As the forest grows, it generates an internal unbiased estimate of the generalization error.
• Provides a good strategy for guessing missing data that maintains its accuracy even in cases of massive data loss.
• Includes methods for balancing the inaccuracies of uneven datasets in class populations.
• The forest created can be saved and used for other data in the future.
• Create prototypes to show relationships between variables and categories.
• Calculate the distance between pairs of examples, which is useful for clustering, detecting outliers, or providing an engaging view of the data (to scale).
• Unlabeled data can be used to create unsupervised clustering, data visualization, and outlier identification using the features described above.
• Provides a mechanism for experimentally finding variable interactions.

When we train a random forest model on a data set with specific characteristics, the resulting model object can tell us which features are most relevant during the training process, that is, which features has the greatest impact on the target variable. The importance of this variable is determined for each tree in the random forest and then averaged across the forest to produce a single measure for each feature. This metric can be used to sort features by relevance and retrain our random forest model using only these features.

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