Based on R, for self-consumption only.
Believe in yourself and learn a little more every day.
Prediction of continuous dependent variables:
stats package
lm function, implements multiple linear regression
stats package
glm function, implements generalized linear regression
stats package
nls function, implements nonlinear least squares regression
rpart package
rpart function, CART algorithm-based classification regression tree model
RWeka Package
M5P function, model tree algorithm, advantages of set linear regression and CART algorithm
adabag package
bagging function, ensemble algorithm based on rpart algorithm
adabag package
boosting function, ensemble algorithm based on rpart algorithm
randomForest package
randomForest function, ensemble algorithm based on rpart algorithm
e1071 Package
svm function, support vector machine algorithm
kernlab package
ksvm function, support vector machine based on kernel function
nnet package
nnet function, single hidden layer neural network algorithm
neuralnet package
neuralnet function, multi-hidden layer and multi-node neural network algorithm
RSNNS package
mlp function , the Multilayer Perceptron Neural Network
RSNNS package
rbf function, a neural network based on radial basis functions
Classification of discrete dependent variables:
stats package
glm function, implement Logistic regression, choose logit connection function
stats package
knn function, k nearest neighbor algorithm
kknn package
kknn function, weighted k nearest neighbor algorithm
rpart package
rpart function, CART algorithm-based classification regression Tree model
adabag package
bagging function, ensemble algorithm based on rpart algorithm
adabag package
boosting function, ensemble algorithm based on rpart algorithm
randomForest package
randomForest function, ensemble algorithm based on rpart algorithm
party package
ctree function, conditional classification tree algorithm
RWeka package
OneR function, one dimensional learning rule algorithm
RWeka package
JPip function, multi-dimensional learning rule algorithm
RWeka package
J48 function, decision tree based on C4.5 algorithm
C50 package
C5.0 function, decision tree based on C5.0 algorithm
e1071 package
svm function, support vector Machine algorithm
kernlab package
ksvm function, support vector machine based on kernel function
e1071 package
naiveBayes function, Bayesian classifier algorithm
klaR package
NaiveBayes function, Bayesian classifier score
MASS package
lda function, linear discriminant analysis
MASS package
qda function, quadratic discriminant analysis
nnet package
nnet function, single hidden layer neural network algorithm
RSNNS package
mlp function, multilayer perceptron neural network
RSNNS package
rbf function, a neural network based on radial basis functions
Clustering: The Nbclust function of the
Nbclust package
can determine which types of
stats package
kmeans functions, k-means clustering algorithm
cluster package
pam function, k center point clustering algorithm
stats package
hclust function, hierarchical clustering algorithm
fpc package
dbscan function, density The clustering algorithm
fpc package
kmeansruns function is more stable than kmeans function, and it can also be estimated to be clustered into several types of
fpc package
pamk function. Compared with pam function, it can give the number of clusters for reference
mclust package
Mclust Function, Expectation Maximum (EM) Algorithm
Association rules:
arules package
apriori function, Apriori association rule algorithm