Machine Learning Algorithms (1)

Based on the similarity of functions and forms of algorithms, we can classify algorithms, such as tree-based algorithms and neural network-based algorithms. Of course, the scope of machine learning is very large, and it is difficult for some algorithms to be clearly classified into a certain category. For some categories, the same classification algorithm can address different types of problems. Here, we try to classify common algorithms in the easiest way to understand.

(1)Regression Algorithm:

Regression Algorithms are a type of algorithms that try to measure the error to explore the relationship between variables. Regression Algorithms are a powerful tool for machine learning statistics. In the field of machine learning, people talk about regression, which sometimes refers to a type of problem or a type of algorithm, which often puzzles beginners. Common Regression Algorithms include ordinary least square, logistic regression, stepwise regression, and multivariate adaptive regression splines) and local scatter smoothing (locally estimated scatterplot smoothing ).

(2) instance-based algorithms

Instance-based algorithms are often used to create models for decision-making problems. Such models often select a batch of sample data first, and then compare new data with the sample data based on some approximation. This method is used to find the best match. Therefore, instance-based algorithms are often referred to as "Winner-free" learning or "memory-based learning ". Common algorithms include K-Nearest Neighbor (KNN), learning vector quantization (LVQ), and self-organizing Map (SOM ).

(3)Regularization Method

The regularization method is an extension of other algorithms (usually Regression Algorithms). The algorithm is adjusted based on the complexity of the algorithm. Normalization methods usually reward simple models and punish complex algorithms. Common algorithms include ridge regression, least absolute shrinkage and selection operator (lasso), and elastic network (elastic net ).

(4)Decision Tree Learning

The decision tree algorithm uses a tree structure to establish a Decision Model Based on data attributes. The decision tree model is often used to solve classification and Regression Problems. Common algorithms include: Classification and regression tree, cart, ID3 (iterative dichotomiser 3), C4.5, Chi-squared automatic IC interaction detection (chaid), demo-stump, random forest (random forest), multivariate adaptive regression spline (MARS), and gradient boosting machine (GBM ).

Machine Learning Algorithms (1)