【R】如何确定最适合数据集的机器学习算法

抽查(Spot checking)机器学习算法是指如何找出最适合于给定数据集的算法模型。本文中我将介绍八个常用于抽查的机器学习算法，文中还包括各个算法的 R 语言代码，你可以将其保存并运用到下一个机器学习项目中。

适用于你的数据集的最佳算法

抽查哪些算法？

首先，你可以思考哪些算法可能适用于你的数据集。其次，我建议尽可能地尝试混合算法并观察哪个方法最适用于你的数据集。

让我们具体看下如何实现这几个想法。下一章中我们将看到如何在 R 语言中实现相应的机器学习算法。

如何在 R 语言中抽查算法？

1.常规的训练和预测方法2.caret包的用法

你需要知道给定算法对应的软件包和函数，同时你还需了解如何利用caret包实现这些常用的算法，从而你可以利用caret包的预处理、算法评估和参数调优的能力高效地评估算法的精度。本文中将用到两个标准的数据集：

1.回归模型：BHD(Boston Housing Dataset)2.分类模型: PIDD(Pima Indians Diabetes Dataset)

本文中的算法将被分成两组进行介绍：

1.线性算法：简单、较大的偏倚、运算速度快2.非线性算法：复杂、较大的方差、高精确度

下文中的所有代码都是完整的，因此你可以将其保存下来并运用到下个机器学习项目中。

线性算法

这类方法对模型的函数形式有严格的假设条件，虽然这些方法的运算速度快，但是其结果偏倚较大。这类模型的最终结果通常易于解读，因此如果线性模型的结果足够精确，那么你没有必要采用较为复杂的非线性模型。

线性回归模型

stat包中的lm()函数可以利用最小二乘估计拟合线性回归模型。

# load the library library(mlbench) # load data data(BostonHousing) # fit model fit <- lm(mdev~>, BostonHousing) # summarize the fit print(fit) # make predictions predictions <- predict(fit, BostonHousing) # summarize accuracy mse <- mean((BostonHousing$medv - predictions)^2) print(mse)  # caret # load libraries library(caret) library(mlbench) # load dataset data(BostonHousing) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.lm <- train(medv~., data=BostonHousing, method="lm", metric="RMSE", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.lm) 

罗吉斯回归模型

stat包中glm()函数可以用于拟合广义线性模型。它可以用于拟合处理二元分类问题的罗吉斯回归模型。

# load the library library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # fit model fit <- glm(diabetes~., data=PimaIndiansDiabetes, family=binomial(link='logit')) # summarize the fit print(fit) # make predictions probabilities <- predict(fit, PimaIndiansDiabetes[,1:8], type='response') predictions <- ifelse(probabilities > 0.5,'pos','neg') # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.glm <- train(diabetes~., data=PimaIndiansDiabetes, method="glm", metric="Accuracy", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.glm) 

线性判别分析

MASS包中的lda()函数可以用于拟合线性判别分析模型。

# load the libraries library(MASS) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # fit model fit <- lda(diabetes~., data=PimaIndiansDiabetes) # summarize the fit print(fit) # make predictions predictions <- predict(fit, PimaIndiansDiabetes[,1:8])$class # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.lda <- train(diabetes~., data=PimaIndiansDiabetes, method="lda", metric="Accuracy", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.lda) 

正则化回归

glmnet包中的glmnet()函数可以用于拟合正则化分类或回归模型。分类模型：

# load the library library(glmnet) library(mlbench) # load data data(PimaIndiansDiabetes) x <- as.matrix(PimaIndiansDiabetes[,1:8]) y <- as.matrix(PimaIndiansDiabetes[,9]) # fit model fit <- glmnet(x, y, family="binomial", alpha=0.5, lambda=0.001) # summarize the fit print(fit) # make predictions predictions <- predict(fit, x, type="class") # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) library(glmnet) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.glmnet <- train(diabetes~., data=PimaIndiansDiabetes, method="glmnet", metric="Accuracy", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.glmnet) 

回归模型：

# load the libraries library(glmnet) library(mlbench) # load data data(BostonHousing) BostonHousing$chas <- as.numeric(as.character(BostonHousing$chas)) x <- as.matrix(BostonHousing[,1:13]) y <- as.matrix(BostonHousing[,14]) # fit model fit <- glmnet(x, y, family="gaussian", alpha=0.5, lambda=0.001) # summarize the fit print(fit) # make predictions predictions <- predict(fit, x, type="link") # summarize accuracy mse <- mean((y - predictions)^2) print(mse)  # caret # load libraries library(caret) library(mlbench) library(glmnet) # Load the dataset data(BostonHousing) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.glmnet <- train(medv~., data=BostonHousing, method="glmnet", metric="RMSE", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.glmnet) 

非线性算法

# knn direct classification  # load the libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # fit model fit <- knn3(diabetes~., data=PimaIndiansDiabetes, k=3) # summarize the fit print(fit) # make predictions predictions <- predict(fit, PimaIndiansDiabetes[,1:8], type="class") # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.knn <- train(diabetes~., data=PimaIndiansDiabetes, method="knn", metric="Accuracy", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.knn) 

回归模型：

# load the libraries library(caret) library(mlbench) # load data data(BostonHousing) BostonHousing$chas <- as.numeric(as.character(BostonHousing$chas)) x <- as.matrix(BostonHousing[,1:13]) y <- as.matrix(BostonHousing[,14]) # fit model fit <- knnreg(x, y, k=3) # summarize the fit print(fit) # make predictions predictions <- predict(fit, x) # summarize accuracy mse <- mean((BostonHousing$medv - predictions)^2) print(mse)  # caret # load libraries library(caret) data(BostonHousing) # Load the dataset data(BostonHousing) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.knn <- train(medv~., data=BostonHousing, method="knn", metric="RMSE", preProc=c("center", "scale"), trControl=control) # summarize fit print(fit.knn) 

朴素贝叶斯算法e1071包中的naiveBayes()函数可用于拟合分类问题中的朴素贝叶斯模型。

# load the libraries library(e1071) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # fit model fit <- naiveBayes(diabetes~., data=PimaIndiansDiabetes) # summarize the fit print(fit) # make predictions predictions <- predict(fit, PimaIndiansDiabetes[,1:8]) # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.nb <- train(diabetes~., data=PimaIndiansDiabetes, method="nb", metric="Accuracy", trControl=control) # summarize fit print(fit.nb) 

# Classification Example: # load the libraries library(kernlab) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # fit model fit <- ksvm(diabetes~., data=PimaIndiansDiabetes, kernel="rbfdot") # summarize the fit print(fit) # make predictions predictions <- predict(fit, PimaIndiansDiabetes[,1:8], type="response") # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.svmRadial <- train(diabetes~., data=PimaIndiansDiabetes, method="svmRadial", metric="Accuracy", trControl=control) # summarize fit print(fit.svmRadial) 

回归模型：

# Regression Example: # load the libraries library(kernlab) library(mlbench) # load data data(BostonHousing) # fit model fit <- ksvm(medv~., BostonHousing, kernel="rbfdot") # summarize the fit print(fit) # make predictions predictions <- predict(fit, BostonHousing) # summarize accuracy mse <- mean((BostonHousing$medv - predictions)^2) print(mse)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(BostonHousing) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.svmRadial <- train(medv~., data=BostonHousing, method="svmRadial", metric="RMSE", trControl=control) # summarize fit print(fit.svmRadial) 

分类和回归树

rpart包中的rpart()函数可用于拟合CART分类树和回归树模型。分类模型：

# load the libraries library(rpart) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # fit model fit <- rpart(diabetes~., data=PimaIndiansDiabetes) # summarize the fit print(fit) # make predictions predictions <- predict(fit, PimaIndiansDiabetes[,1:8], type="class") # summarize accuracy table(predictions, PimaIndiansDiabetes$diabetes)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(PimaIndiansDiabetes) # train set.seed(7) control <- trainControl(method="cv", number=5) fit.rpart <- train(diabetes~., data=PimaIndiansDiabetes, method="rpart", metric="Accuracy", trControl=control) # summarize fit print(fit.rpart) 

回归模型：

# load the libraries library(rpart) library(mlbench) # load data data(BostonHousing) # fit model fit <- rpart(medv~., data=BostonHousing, control=rpart.control(minsplit=5)) # summarize the fit print(fit) # make predictions predictions <- predict(fit, BostonHousing[,1:13]) # summarize accuracy mse <- mean((BostonHousing$medv - predictions)^2) print(mse)  # caret # load libraries library(caret) library(mlbench) # Load the dataset data(BostonHousing) # train set.seed(7) control <- trainControl(method="cv", number=2) fit.rpart <- train(medv~., data=BostonHousing, method="rpart", metric="RMSE", trControl=control) # summarize fit print(fit.rpart) 

其他算法

总结

本文中介绍了八个常用的机器学习算法：

1. 线性回归模型
2. 罗吉斯回归模型
3. 线性判别分析
4. 正则化回归
5. k近邻
6. 朴素贝叶斯
7. 支持向量机
8. 分类和回归树

从上文的介绍中，你可以学到如何利用 R 语言中的包和函数实现这些算法。同时你还可以学会如何利用caret包实现上文提到的所有机器学习算法。最后，你还可以将这些算法运用到你的机器学习项目中。

你的下一步计划？

你有没有试验过本文中的算法代码？

原文链接： http://machinelearningmastery.com/spot-check-machine-learning-algorithms-in-r/

原文作者：Jason Brownlee

译者：Fibears