Machine Learning Models http://vitalflux.com/cheat-sheet-10-machine-learning-algorithms-r-commands/

What To Know For Machine Learning Overview Algorithms Prediction

Machine Learning Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory inartificial intelligence. Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal. Another example is learning to play a game by playing against an opponent.

Categorization Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system: In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one (or multi-label classification) or more of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are "spam" and "not spam". In regression, also a supervised problem, the outputs are continuous rather than discrete. In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task. Density estimation finds the distribution of inputs in some space. Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.

Algorithms Linear regression Logistic Regression K-Means Clustering K-Nearest Neighbors (KNN) Classification Naive Bayes Classification Decison Trees Support Vector Machine (SVM) Artifical Neural Network (ANN) Apriori AdaBoost

Linear Regression Linear regression: “lm” method from base package could be used for linear regression models. Following is the sample command: lm_model <- lm(y ~ x1 + x2, data=as.data.frame(cbind(y,x1,x2)))

Logistic Regression Logistic Regression: Logistic regression is a classification based model. “glm” method from base R package could be used for logistic regression. Following is the sample command: glm_model <- glm(y ~ x1+x2, family=binomial(link="logit"), data=as.data.frame(cbind(y,x1,x2))) https://en.wikipedia.org/wiki/Logistic_regression http://www.ats.ucla.edu/stat/r/dae/logit.htm

K-Means Clustering K-Means Clustering: “kmeans” method from base R package could be used to run k-means clustering. Following is a sample command given X is a data matrix and m is the number of clusters: kmeans_model <- kmeans(x=X, centers=m) http://www.r-statistics.com/2013/08/k-means-clustering-from-r-in- action/

K-Nearest Neighbours K-Nearest Neighbors (KNN) Classification: “knn” method from “class” package could be used for K-NN modeling. One need to install and load “class” package. Following is the sample command given X_train represents a training dataset, X_test represents test data set, k represents number of nearest neighbors to be included for the modeling knn_model <- knn(train=X_train, test=X_test, cl=as.factor(labels), k=K) https://www.datacamp.com/community/tutorials/machine-learning- in-r

Naive Bayes Classification Naive Bayes Classification: “naiveBayes” method from “e1071” package could be used for Naive Bayes classification. One need to install and load “e1071” package prior to analysis. Following is the sample command: naiveBayes_model <- naiveBayes(y ~ x1 + x2, data=as.data.frame(cbind(y,x1,x2))) https://eight2late.wordpress.com/2015/11/06/a-gentle-introduction- to-naive-bayes-classification-using-r/

Decision Trees Decision Trees: “rpart” method from “rpart” can be used for Decision Trees. One need to install and load “rpart” package. Following is the sample command: cart_model <- rpart(y ~ x1 + x2, data=as.data.frame(cbind(y,x1,x2)), method="class") http://www.statmethods.net/advstats/cart.html

Support Vector Machine Support Vector Machine (SVM): “svm” method from “e1071” package could be used for SVM. Note that the same package also provide method, naiveBayes, for Naive Bayes classification. One need to install and load “e1071” package. Following is the sample command given X is the matrix of features, labels be the vector of 0-1 class labels, and C being regularization parameter. svm_model <- svm(x=X, y=as.factor(labels), kernel ="radial", cost=C) https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classifi cation/SVM

Artificial Neural Network Artifical Neural Network (ANN): “neuralnet” method from “neuralnet” package could be used for ANN modeling. Following is sample command: ann_model <- neuralnet( y ~ x1 + x2 + x3, data=as.data.frame(cbind(y,x1,x2, x3)), hidden = 1) Prediction could be made using following formula: p <- compute( ann_model, as.data.frame(cbind(x1,x2)) ) https://beckmw.wordpress.com/tag/neural-network/

Apriori Apriori: “apriori” method from “arules” package could be used for Apriori analysis. One need to install and load “arules” package. Following is the sample command: apriori_model <- apriori(as.matrix(sampleDataset), parameter = list(supp = 0.8, conf = 0.9)) http://www.r-bloggers.com/association-rule-learning-and-the-apriori- algorithm/

AdaBoost AdaBoost: “ada” method from “rpart” package could be used as boosting function. Following is sample command: boost_model <- ada(x=X, y=labels) https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classifi cation/adaboost

Prediction For most of the above formulas including linear regression model, one could use following function to predict: predicted_values <- predict(some_model, newdata=as.data.frame(cbind(x1_test, x2_test))) http://www.dummies.com/how-to/content/how-to-predict-new- data-values-with-r.html http://astrostatistics.psu.edu/datasets/R/html/stats/html/predict.lm. html