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Study excerpts and notes (12) --- " Top 10 Algorithms in the Field of Artificial Intelligence"
Top 10 Algorithms in Artificial Intelligence
Source of original text/paper:
Title: " Top 10 Algorithms in the Field of Artificial Intelligence"
Author: AI Outsmarts the Future
Time: 2022-09-26
Source: AI Outsmarts the Future
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 .
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. The Y value in the logistic function ranges from 0 to 1 and is a probability value . Logistic functions usually have an S-shaped curve that divides the graph into two regions , making them suitable for classification tasks.
3 decision trees
If both linear and logistic regression end the task within one round, then Decision Trees is a multi-step action that is also used in regression and classification tasks, but the scenarios are usually more complex and specific.
4 Naive Bayes
Naive Bayes (Naive Bayes) is based on Bayes' theorem, that is, between two conditional relations . It measures the probability of each class, and the conditional probability of each class gives the value of x. This algorithm is used in classification problems to obtain a binary "yes/no" result.
5 Support Vector Machines
Support Vector Machine (SVM) is a supervised algorithm for classification problems . The SVM tries to draw two lines between the data points with the largest margin between them. To do this, we plot data items as points in n-dimensional space, where n is the number of input features. On this basis, the support vector machine finds an optimal boundary , called the hyperplane, which best separates the possible outputs by class labels.
6 K-nearest neighbor algorithm
K-Nearest Neighbors (KNN) is very simple. KNN classifies objects by searching for the K most similar instances , or K neighbors, across the entire training set , and assigning a common output variable to all of these K instances.
The choice of K is critical: smaller values may give a lot of noise and inaccurate results, while larger values are infeasible. It is most commonly used for classification, but is also applicable to regression problems.
7 K-means
K-means (K-means) is clustering by classifying the data set . For example, this algorithm can be used to group users based on purchase history. It finds K clusters in the dataset. K-means is used for unsupervised learning, therefore, we only need to use the training data X, and the number K of clusters we want to identify.
8 random forest
Random Forest is a very popular ensemble machine learning algorithm . The basic idea behind this algorithm is that the opinions of many people are more accurate than the opinions of one individual . In random forests, a decision tree ensemble is used (see Decision Trees).
(a) During training, each decision tree is constructed based on bootstrap samples from the training set .
(b) During classification, the decision to input instances is made based on a majority vote .
Random forest has a wide range of application prospects, from marketing to health care insurance. It can be used to model marketing simulations, count customer sources, retention and loss, and can also be used to predict the risk of disease and the susceptibility of patients .
9 Dimensionality reduction
Dimensionality reduction attempts to solve this problem by combining specific features into higher-level features without losing the most important information. Principal Component Analysis (PCA) is the most popular dimensionality reduction technique.
1 0 artificial neural network
Artificial Neural Networks (ANN) can handle large and complex machine learning tasks . A neural network is essentially a set of interconnected layers of weighted edges and nodes called neurons .
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