First, we introduce the learning algorithm of support vector machine. In fact, the support vector machine algorithm is called SVM for short. Generally speaking, support vector machine algorithm is a supervised machine learning algorithm for classification or regression problems. SVM learns from data sets, so SVM can classify any new data. In addition, it works by classifying data into different categories through searching. We use it to divide the training data set into several categories. Moreover, there are many such linear hyperplanes, and SVM tries to maximize the distance between classes, which is called marginal maximization. Support vector machine algorithms are divided into two categories. The first category is linear SVM. In linear SVM, training data must pass through hyperplane separation classifier. The second is nonlinear SVM. In nonlinear SVM, it is impossible to use hyperplane to separate training data.
Then we will introduce the Apriori machine learning algorithm to you. It should be noted that this is an unsupervised machine learning algorithm. We use it to generate association rules from a given data set. Association rules mean that if item A occurs, item B also occurs with a certain probability, and the generated association rules are mostly in IF_THEN format. The basic principle of Apriori machine learning algorithm is that if itemsets appear frequently, all subsets of itemsets also appear frequently.
Then we will introduce the decision tree machine learning algorithm to you. Decision tree is actually a graphic representation, which uses branching method to explain all possible results of decision. In decision tree, internal nodes represent the test of attributes. Because each branch of the tree represents the test result, and the leaf node represents a specific class label, that is, the decision made after calculating all the attributes. In addition, we must represent the classification by the path from the root node to the leaf node.
Random forest machine learning algorithm is also an important algorithm and the first choice for machine learning. We use bagging method to create a series of decision trees with random data subsets. We must train the model many times on the random samples of the data set, because we need to get good prediction performance from the random forest algorithm. In addition, in this integrated learning method, we must synthesize the outputs of all decision trees and make the final prediction. In addition, we get the final prediction by polling the results of each decision tree.
This paper introduces the algorithms of machine learning, including random forest machine learning algorithm, decision tree algorithm, apriori algorithm and support vector machine algorithm. I believe that after reading this article, everyone will have a more comprehensive understanding of machine learning. Finally, I wish you all a successful return.