Machine Learning Classic Algorithms

function names are functions in the Sklearn library

1. Linear regression algorithm: linearregression:

Among the commonly used are: Ridge: Ridge regression algorithm, Multitasklasso: Multi-task Lasso regression algorithm, elasticnet: Elastic mesh algorithm, lassolars:lars lasso algorithm, Orthogonalmatchingpursuit: Orthogonal matching tracking (OMP) algorithm,

Bayesianridge: Bayesian ridge regression algorithm, logisticregression: Logistic regression algorithm, SGDCLASSIFIER:SGD random gradient descent algorithm, Mutitaskelasticnet: Multi-tasking elastic mesh algorithm, LARS: Minimum angle regression algorithm, Perceptron: Perceptron calculation

method, Passiveaggressiveclassifier:pa passive perception algorithm, Ransacregressor: Robust regression algorithm, Huberregressor:huber regression algorithm

2. Naive Bayesian algorithm, multinaomial Naive Bayes, function name: MULTINOMIALNB

Commonly used: MULTINOMIALNB: Polynomial naive Bayesian algorithm, GAUSSIANNB: Gaussian naive Bayesian algorithm, Bernoullinb: Bo effort naive Bayesian algorithm

3.kNN nearest neighbor algorithm: Kneighborsclassifier

Common: KNEIGHBORSCLASSIFIER:KNN nearest neighbor algorithm, nearestneighbors: Nearest neighbor algorithm, kneighborsregressor:k nearest neighbor algorithm, nearestcentroid: Nearest centroid algorithm

4. Logistic regression algorithm: logisticregression

5. Stochastic forest algorithm, random Forest Classifier:randomforestclassfier

One of the most commonly used: Randomforestclassifier: Random forest algorithm, baggingcclassifier:bagging bagging algorithm

6. Decision Tree algorithm, decision Tree:tree.DecisionTreeClassifier

7.GBDT Iterative decision Tree algorithm, Gradient boosting decision tree, also called Mart (multiple Additive regession tree): Gradientboostingclassifier

8.SVM vector machine algorithm: SVC

9.svm-cross Vector machine crossover algorithm: SVC

Machine Learning Classic Algorithms