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FAQs

Random forest is a supervised machine learning algorithm that is commonly used to solve classification and regression problems. It creates decision trees from various samples, using the majority vote for classification and the average for regression.

Because we are working with subsets of data, random forests works well with high-dimensional data. Because we are only working with a subset of features in our model, it is faster to train than decision trees. We can easily work with hundreds of features.

Random forest is a supervised machine learning algorithm that is commonly used to solve classification and regression problems. It creates decision trees from various samples, using the majority vote for classification and the average for regression.

Reduce the number of estimators in your random forest to speed it up. Increase the number of trees in your model if you want it to be more accurate. Set the maximum amount of features that should be included in each node split. This is highly dependent on your dataset.

Random Forest's Advantages and Disadvantages:

  • It helps to improve decision tree accuracy by reducing overfitting.
  • It can handle both classification and regression problems with ease.
  • It can handle both category and continuous data.
  • It automates the process of filling in missing values in data.

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