A review of supervised classification algorithms and how they work

Algorithms used for supervised classification can classify and predict data and are one of the most commonly used algorithms in the field of machine learning. These algorithms can classify data in different fields, such as image recognition, speech recognition, credit assessment, risk analysis, etc. Supervised classification algorithms can help companies, institutions, and individuals conduct data analysis and decision-making, such as predicting consumer purchasing behavior through classification, judging the health status of patients, identifying spam, etc. In addition, these algorithms can also be used in natural language processing, machine translation, robot control and other fields. In short, algorithms for supervised classification are widely used in various fields and are of great significance for improving work efficiency and decision-making quality.

The following are some common algorithms used for supervised classification and an introduction to their principles:

Decision tree: According to different characteristics of the data, it is divided into Multiple areas corresponding to different categories.

The Naive Bayes classifier uses Bayes' theorem, prior probability and conditional probability to classify data, assuming that each feature is independent of each other.

Support vector machine is an algorithm that separates different categories of data by building a hyperplane. It improves classification accuracy by maximizing the distance of the hyperplane to the nearest data points. In two dimensions, a hyperplane can be viewed as a straight line.

Logistic regression: This algorithm uses a logistic function to build a classification model. The input of the logistic function is the weighted sum of feature values, the output is the probability of belonging to a certain class, and the result of the classification is the probability Data points greater than a threshold belong to this category.

Random forest: This algorithm combines multiple decision trees to form a forest. Each decision tree independently classifies the data, and finally determines the final classification result through voting.

Nearest neighbor algorithm: This algorithm compares new data with known data to find the closest data point. The classification of this point is the classification of the new data.

Neural network: This algorithm classifies data by building multiple layers of neurons (nodes). Each neuron determines itself by learning the relationship between input data and output data. the weight of.

AdaBoost algorithm: This algorithm iteratively trains multiple weak classifiers (classification accuracy is slightly higher than random guessing), and then combines these weak classifiers into a strong classifier, each time Each iteration adjusts the weights of the data set so that misclassified data points receive higher weights.

Gradient boosting algorithm: This algorithm also trains weak classifiers iteratively and combines them into strong classifiers. The difference is that it adjusts the parameters of the classifier through gradient descent. .

Linear discriminant analysis: This algorithm projects data into a low-dimensional space to separate different categories of data as much as possible, and then projects new data into this space for classification .

Ensemble learning algorithms: These algorithms improve classification accuracy by combining multiple classifiers, such as Bagging and Boosting.

Multi-category classification algorithms: These algorithms are used to handle classification problems of multiple categories, such as one-to-many and one-to-one classification methods.

Deep learning algorithm: This algorithm classifies data by building a multi-layer neural network, including convolutional neural networks and recurrent neural networks.

Decision rule algorithm: This algorithm classifies data by generating a set of rules, such as the C4.5 and CN2 algorithms.

Fisher discriminant analysis algorithm: This algorithm performs classification by maximizing the distance between categories and minimizing the variance within the categories.

Linear regression algorithm: This algorithm classifies data by establishing a linear model. The linear model is a function of the weighted sum of feature values.

Decision Forest Algorithm: This algorithm is a variant of random forest. It uses the idea of ​​random subspace and uses different features for each decision tree during the training process. set.

Perceptron algorithm: This algorithm determines a hyperplane by learning the relationship between input data and output data, dividing the data into two categories.

Mixture Gaussian model algorithm: This algorithm uses multiple Gaussian distributions to model the data, each Gaussian distribution corresponding to a category.

Improved KNN algorithm: This algorithm uses the KNN algorithm for classification, but for missing feature values, it uses the KNNImpute algorithm to fill in, and uses the KNN algorithm to reduce the impact of noise.

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