1. Linear regression
Linear Regression is probably the most popular machine learning algorithm. Linear regression is to find a straight line and make it fit the data points in the scatter plot as closely as possible. It attempts to represent independent variables (x values) and numerical outcomes (y values) by fitting the equation of a line to the data. This line can then be used to predict future values!
The most commonly used technique for this algorithm is Least of squares. This method calculates the line of best fit such that the vertical distance to each data point on the line is minimized. The total distance is the sum of the squares of the vertical distances (green line) of all data points. The idea is to fit the model by minimizing this squared error or distance.
2. Logistic regression
Logistic regression is similar to linear regression, but the result of logistic regression can only have two values. If linear regression is predicting an open value, then logistic regression is more like a yes or no judgment question. Logistic regression is often used by e-commerce or food delivery platforms to predict users' purchase preferences for categories.
3. Linear Discriminant Analysis
Linear Discriminant Analysis (LDA), also known as Fisher Linear Discriminant (FLD), is a classic pattern recognition algorithm, which was introduced into the field of pattern recognition and artificial intelligence by Belhumeur in 1996. The basic idea of discriminant analysis is to project high-dimensional pattern samples into the best discriminative vector space to achieve the effect of extracting classification information and compressing the dimension of feature space, and after projection, pattern samples are guaranteed to have the largest class gap in the new subspace. distance and the smallest within-class distance, i.e., the patterns have the best separability in that space. Therefore, it is an effective feature extraction method.
4. Decision tree
Decision tree is an algorithm that classifies and predicts test data by measuring and computing training data.
A decision tree usually consists of 3 main parts, which are decision nodes, branches, and leaf nodes. The decision node at the top of the decision tree is the root decision node. Each branch has a new decision node. Below the decision nodes are the leaf nodes. Each decision node represents a data category or attribute to be classified, and each leaf node represents a result. The entire decision-making process starts from the root decision node, from top to bottom. Different results are given at each decision node according to the classification of the data.
5. Learn vector quantization
Learning Vector Quantization (LVQ) is a supervised learning algorithm for pattern classification and a supervised neural network classification algorithm with simple structure and powerful functions.
6. Support Vector Machines
Support Vector Machine (SVM) is a supervised algorithm for classification problems. The optimization of the generalization ability in the case of limited training samples is achieved by using the maximum classification interval criterion. Non-linear classification or functional regression is realized indirectly through the kernel function, and the support vector machine is usually abbreviated as SVM.
7. Nearest Neighbor Algorithm
K-Nearest Neighbors (KNN) is very simple. KNN classifies objects by searching for the K most similar instances, or K neighbors, in the entire training set, and assigning a common output variable to all these K instances.
8. Random Forest Algorithm
Random Forest is a very popular ensemble machine learning algorithm. It is an algorithm that integrates multiple trees through the idea of ensemble learning. Its basic unit is a decision tree, and its essence belongs to a branch of machine learning——Ensemble Learning (Ensemble Learning) method. In integrated learning, it is mainly divided into bagging algorithm and boosting algorithm, and the random forest here mainly uses the bagging algorithm.
9. Artificial neural network
The artificial neural network algorithm simulates the biological neural network and is a kind of pattern matching algorithm. Often used to solve classification and regression problems. Artificial neural networks are a vast branch of machine learning with hundreds of different algorithms.
10. Bayesian algorithm
Naive Bayes (Naive Bayes, NB) algorithm is a classification method based on Bayesian theorem and the independent assumption of characteristic conditions. This algorithm is a supervised learning algorithm, which solves the classification problem and divides an unknown sample into several A process of classes known in advance.
The idea of Naive Bayes is to calculate the posterior probability of the Y variable belonging to a certain category based on certain prior probabilities, that is, to estimate the probability of an event occurring in the future based on the relevant data of previous events.
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