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Abstract Basic definition of random forest
Steps of random forest implementation
MNIST data set classification and recognition
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effect diagram
result analysis
outlook
reference paper
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The MNIST data set is a commonly used data set used to detect the performance of algorithms. Random forest is one of the algorithms with better classification ability in classification algorithms. In this paper, random forest is used for classification.
Summary
The principle of random forest, MATALB programming random forest mnist data set classification recognition
Basic Definition of Random Forest
In machine learning, a random forest is a classifier that contains multiple decision trees, and its output category is determined by the mode of the category output by individual trees. Leo Breiman and Adele Cutler developed algorithms to infer random forests. And "Random Forests" is their trademark. The term comes from random decision forests proposed by Tin Kam Ho of Bell Labs in 1995. This method combines Breimans' "Bootstrap aggregating" idea with Ho's "random subspace method" to build an ensemble of decision trees.
training method
Each tree is constructed according to the following algorithm [1]:
use N to represent the number of training cases (samples), and M to represent the number of features.
Enter the number of features m,