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Cheat Sheet - 10 Machi ne ne Learning Algorithms & R Commands - Bytes Cravings
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Cheat Sheet – 10 Machine Machin e Learning Algorithms & R Commands
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Cheat Sheet – 10 Machine Learning Algorithms & R Commands BY AJITESH KUMAR ON KUMAR ON JANUARY 16, 2015
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This article lists down 10 popular machine learning algorithms and algorithms and related R commands commands (& (& package information) that could be used to create respective models. The objective is to represent represent a quick reference reference page for
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beginners/intermediate level R programmers who working on machine learning related pr problems. Please feel free to comment/suggest if I missed to mention one or more important points. points. Also, sorry for the the typos.
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Following are the dierent ML algorithms included in this article:
1. Linear regression 2. Logistic Regression 3. K-Means Clustering
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4. K-Nearest Neighbors (KNN) Classication 5. Naive Bayes Classication 6. Decison Decison Trees Trees 7. Support Vector Machine (SVM)
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8. Artical Neural Network Network (ANN) 9. Apriori 10. AdaBoost
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Cheat Sheet – ML Algorithms & R Commands Linear regression: regression : “lm” method from base package could be used for linear regression models. Following is
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the sample command:
lm_model <- lm(y ~ x1 + x2, data=as.data.frame(cbind(y,x1,x2))) data=as.data.frame(cbind(y,x1,x2)))
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Logistic Regression: Regression : Logistic regression is a classication 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))) data=as.data.frame(cbind(y,x1,x2)))
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K-Means Clustering: 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) K-Nearest Neighbors (KNN) Classicati Classication on:: “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
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be included for the modeling
knn_model <- knn(train=X_train, test=X_test, cl=as.factor(labels), cl=as.factor(labels), k=K)
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Naive Bayes Classicati Classication on:: “naiveBayes” method from “e1071” package could be used for Naive Bayes classication. One need to install and load “e1071” package prior to analysis. Following is the sample command:
naiveBayes_model naiveBayes_mod el <- naiveBayes(y ~ x1 + x2, data=as.data.frame(cbind(y,x1,x2))) data=as.data.frame(cbind(y,x1,x2))) Decision Trees: Trees: “rpart” method from “rpart” can be used for Decision Trees. One need to install and load
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10/18/2016
Cheat Sheet - 10 Machi ne Learning Algorithms & R Commands - Bytes Cravings
“rpart” package. Following is the sample command:
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cart_model <- rpart(y ~ x1 + x2, data=as.data.frame(cbind(y,x1,x2)), method="class") Support Vector Machine (SVM): “svm” method from “e1071” package could be used for SVM. Note that the
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same package also provide method, naiveBayes, for Naive Bayes classication. 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
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0-1 class labels, and C being regularization parameter
svm_model <- svm(x=X, y=as.factor(labels), kernel ="radial", cost=C) Artical 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)
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Prediction could be made using following formula:
p <- compute( ann_model, as.data.frame(cbind(x1,x2)) )
Apriori: “apriori” method from “arules” package could be used for Apriori analysis. One need to install and
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load “arules” package. Following is the sample command:
apriori_model <- apriori(as.matrix(sampleDataset), parameter = list(supp = 0.8, conf = 0.9)) AdaBoost: “ada” method from “rpart” package could be used as boosting function. Following is sample command:
boost_model <- ada(x=X, y=labels) 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)))
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Ajitesh Kumar Ajitesh is passionate about various dierent technologies including programming languages such as Java/JEE, Javascript, PHP, .NET, C/C++, mobile programming languages etc and, computing fundamentals such as application security, cloud computing, API, mobile apps, google glass, big data etc.Recently, he has been digging deep into the eld of data science and machine learning. Follow him on Twitter and Google+.
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