常用的机器学习算法（使用 Python 和 R 代码）_机器学习lr的r语言代码

R代码最常用的10种机器学习算法在Python和R中的代码对比：

① 线性回归算法（Linear Regression）

② 逻辑回归算法（Logistic Regression）

③ 决策树算法（Decision Tree

④ 支持向量机算法（SVM）

⑤ 朴素贝叶斯算法（Naive Bayes）

⑥ K邻近算法（k- Nearest Neighbors,kNN）

⑦ K均值算法（k-Means）

⑧ 随机森林算法（Random Forest）

⑨ 主成分分析算法（PCA）

⑩ 梯度提升树（Gradient Boosting）

1. GBM
2. XGBoos
3. LightGBM
4. CatBoost

一、线性回归算法（Linear Regression）

线性回归主要有两种类型：简单线性回归和多元线性回归。简单线性回归的特征是一个自变量。而且，多元线性回归（顾名思义）的特征是多个（超过1个）自变量。在查找最佳拟合线时，可以拟合多项式或曲线回归。这些被称为多项式或曲线回归。

1、Python代码

# importing required libraries
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
 
# read the train and test dataset
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
 
print(train_data.head())
 
# shape of the dataset
print('\nShape of training data :',train_data.shape)
print('\nShape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Item_Outlet_Sales
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Item_Outlet_Sales'],axis=1)
train_y = train_data['Item_Outlet_Sales']
 
# seperate the independent and target variable on training data
test_x = test_data.drop(columns=['Item_Outlet_Sales'],axis=1)
test_y = test_data['Item_Outlet_Sales']
 
'''
Create the object of the Linear Regression model
You can also add other parameters and test your code here
Some parameters are : fit_intercept and normalize
Documentation of sklearn LinearRegression: 
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
 '''
model = LinearRegression()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# coefficeints of the trained model
print('\nCoefficient of model :', model.coef_)
 
# intercept of the model
print('\nIntercept of model',model.intercept_)
 
# predict the target on the test dataset
predict_train = model.predict(train_x)
print('\nItem_Outlet_Sales on training data',predict_train) 
 
# Root Mean Squared Error on training dataset
rmse_train = mean_squared_error(train_y,predict_train)**(0.5)
print('\nRMSE on train dataset : ', rmse_train)
 
# predict the target on the testing dataset
predict_test = model.predict(test_x)
print('\nItem_Outlet_Sales on test data',predict_test) 
 
# Root Mean Squared Error on testing dataset
rmse_test = mean_squared_error(test_y,predict_test)**(0.5)
print('\nRMSE on test dataset : ', rmse_test)

2、R代码

#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train <- input_variables_values_training_datasets
y_train <- target_variables_values_training_datasets
x_test <- input_variables_values_test_datasets
x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary(linear)
#Predict Output
predicted= predict(linear,x_test)

二、逻辑回归算法（Logistic Regression）

不要被它的名字弄糊涂了！它是一种分类，而不是回归算法。它用于根据给定的自变量集估计离散值（二进制值，如0/1，是/否，真/假）。简而言之，它通过将数据拟合到logit函数来预测事件发生的概率。因此，它也被称为logit回归。由于它预测概率，因此其输出值介于 0 和 1 之间（如预期的那样）。

同样，让我们通过一个简单的例子来尝试理解这一点。

假设你的朋友给了你一个谜题来解决。只有2个结果场景 - 要么你解决它，要么你不解决它。现在想象一下，你正在接受各种各样的谜题/测验，试图了解你擅长哪些主题。这项研究的结果将是这样的 - 如果你得到一个基于三角学的十年级问题，你有70%的1可能性来解决它。另一方面，如果是五年级历史问题，得到答案的概率只有30%。这就是 Logistic 回归为您提供的。

1、Python代码

# importing required libraries
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
 
print(train_data.head())
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the Logistic Regression model
You can also add other parameters and test your code here
Some parameters are : fit_intercept and penalty
Documentation of sklearn LogisticRegression: 
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
 '''
model = LogisticRegression()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# coefficeints of the trained model
print('Coefficient of model :', model.coef_)
 
# intercept of the model
print('Intercept of model',model.intercept_)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

2、R代码

x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
logistic <- glm(y_train ~ ., data = x,family='binomial')
summary(logistic)
#Predict Output
predicted= predict(logistic,x_test)

三、决策树算法（Decision Tree）

1、Python代码

# importing required libraries
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the Decision Tree model
You can also add other parameters and test your code here
Some parameters are : max_depth and max_features
Documentation of sklearn DecisionTreeClassifier: 
https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
 '''
model = DecisionTreeClassifier()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# depth of the decision tree
print('Depth of the Decision Tree :', model.get_depth())
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

2、R代码

library(rpart)
x <- cbind(x_train,y_train)
# grow tree 
fit <- rpart(y_train ~ ., data = x,method="class")
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

四、支持向量机算法（SVM）

1. Python代码

# importing required libraries
import pandas as pd
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the Support Vector Classifier model
You can also add other parameters and test your code here
Some parameters are : kernal and degree
Documentation of sklearn Support Vector Classifier: 
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
 '''
model = SVC()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

1. R代码

library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-svm(y_train ~ ., data = x)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

五、朴素贝叶斯算法（Naive Bayes）

1. Python代码

# importing required libraries
import pandas as pd
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the Naive Bayes model
You can also add other parameters and test your code here
Some parameters are : var_smoothing
Documentation of sklearn GaussianNB: 
https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
 '''
model = GaussianNB()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

1. R代码

library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-naiveBayes(y_train ~ ., data = x)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

六、K邻近算法（k- Nearest Neighbors,kNN）

1、 Python代码

# importing required libraries
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the K-Nearest Neighbor model
You can also add other parameters and test your code here
Some parameters are : n_neighbors, leaf_size
Documentation of sklearn K-Neighbors Classifier: 
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
 '''
model = KNeighborsClassifier()  
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# Number of Neighbors used to predict the target
print('\nThe number of neighbors used to predict the target : ',model.n_neighbors)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

2、 R代码

library(knn)
x <- cbind(x_train,y_train)
# Fitting model
fit <-knn(y_train ~ ., data = x,k=5)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

七、K均值算法（k-Means）

1、 Python代码

# importing required libraries
import pandas as pd
from sklearn.cluster import KMeans
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to divide the training data into differernt clusters
# and predict in which cluster a particular data point belongs.  
 
'''
Create the object of the K-Means model
You can also add other parameters and test your code here
Some parameters are : n_clusters and max_iter
Documentation of sklearn KMeans: 
https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
 '''
 
model = KMeans()  
 
# fit the model with the training data
model.fit(train_data)
 
# Number of Clusters
print('\nDefault number of Clusters : ',model.n_clusters)
 
# predict the clusters on the train dataset
predict_train = model.predict(train_data)
print('\nCLusters on train data',predict_train) 
 
# predict the target on the test dataset
predict_test = model.predict(test_data)
print('Clusters on test data',predict_test) 
 
# Now, we will train a model with n_cluster = 3
model_n3 = KMeans(n_clusters=3)
 
# fit the model with the training data
model_n3.fit(train_data)
 
# Number of Clusters
print('\nNumber of Clusters : ',model_n3.n_clusters)
 
# predict the clusters on the train dataset
predict_train_3 = model_n3.predict(train_data)
print('\nCLusters on train data',predict_train_3) 
 
# predict the target on the test dataset
predict_test_3 = model_n3.predict(test_data)
print('Clusters on test data',predict_test_3) 

2、 R代码

library(cluster)
fit <- kmeans(X, 3) # 5 cluster solution

八、随机森林算法（Random Forest）

1、 Python代码

# importing required libraries
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# view the top 3 rows of the dataset
print(train_data.head(3))
 
# shape of the dataset
print('\nShape of training data :',train_data.shape)
print('\nShape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the Random Forest model
You can also add other parameters and test your code here
Some parameters are : n_estimators and max_depth
Documentation of sklearn RandomForestClassifier: 
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
'''
model = RandomForestClassifier()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# number of trees used
print('Number of Trees used : ', model.n_estimators)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

2、 R代码

library(randomForest)
x <- cbind(x_train,y_train)
# Fitting model
fit <- randomForest(Species ~ ., x,ntree=500)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

九、主成分分析算法（PCA）

1、 Python代码

# importing required libraries
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error  
 
# read the train and test dataset
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
 
# view the top 3 rows of the dataset
print(train_data.head(3))
 
# shape of the dataset
print('\nShape of training data :',train_data.shape)
print('\nShape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
# target variable - Item_Outlet_Sales
train_x = train_data.drop(columns=['Item_Outlet_Sales'],axis=1)
train_y = train_data['Item_Outlet_Sales']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Item_Outlet_Sales'],axis=1)
test_y = test_data['Item_Outlet_Sales']
 
print('\nTraining model with {} dimensions.'.format(train_x.shape[1]))
 
# create object of model
model = LinearRegression()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
 
# Accuray Score on train dataset
rmse_train = mean_squared_error(train_y,predict_train)**(0.5)
print('\nRMSE on train dataset : ', rmse_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
 
# Accuracy Score on test dataset
rmse_test = mean_squared_error(test_y,predict_test)**(0.5)
print('\nRMSE on test dataset : ', rmse_test)
 
# create the object of the PCA (Principal Component Analysis) model
# reduce the dimensions of the data to 12
'''
You can also add other parameters and test your code here
Some parameters are : svd_solver, iterated_power
Documentation of sklearn PCA:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
'''
model_pca = PCA(n_components=12)
 
new_train = model_pca.fit_transform(train_x)
new_test  = model_pca.fit_transform(test_x)
 
print('\nTraining model with {} dimensions.'.format(new_train.shape[1]))
 
# create object of model
model_new = LinearRegression()
 
# fit the model with the training data
model_new.fit(new_train,train_y)
 
# predict the target on the new train dataset
predict_train_pca = model_new.predict(new_train)
 
# Accuray Score on train dataset
rmse_train_pca = mean_squared_error(train_y,predict_train_pca)**(0.5)
print('\nRMSE on new train dataset : ', rmse_train_pca)
 
# predict the target on the new test dataset
predict_test_pca = model_new.predict(new_test)
 
# Accuracy Score on test dataset
rmse_test_pca = mean_squared_error(test_y,predict_test_pca)**(0.5)
print('\nRMSE on new test dataset : ', rmse_test_pca)

2、 R代码

library(stats)
pca <- princomp(train, cor = TRUE)
train_reduced  <- predict(pca,train)
test_reduced  <- predict(pca,test)

十、梯度提升树（Gradient Boosting）

1. GBM

1、 Python代码

# importing required libraries
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the GradientBoosting Classifier model
You can also add other parameters and test your code here
Some parameters are : learning_rate, n_estimators
Documentation of sklearn GradientBoosting Classifier: 
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
'''
model = GradientBoostingClassifier(n_estimators=100,max_depth=5)
 
# fit the model with the training data
model.fit(train_x,train_y)
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

2、 R代码

library(caret)
x <- cbind(x_train,y_train)
# Fitting model
fitControl <- trainControl( method = "repeatedcv", number = 4, repeats = 4)
fit <- train(y ~ ., data = x, method = "gbm", trControl = fitControl,verbose = FALSE)
predicted= predict(fit,x_test,type= "prob")[,2] 

1. XGBoost

1、  Python代码

# importing required libraries
import pandas as pd
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
 
# Now, we need to predict the missing target variable in the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']
 
'''
Create the object of the XGBoost model
You can also add other parameters and test your code here
Some parameters are : max_depth and n_estimators
Documentation of xgboost:
https://xgboost.readthedocs.io/en/latest/
'''
model = XGBClassifier()
 
# fit the model with the training data
model.fit(train_x,train_y)
 
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

2、  R代码

require(caret)
 
x <- cbind(x_train,y_train)
 
# Fitting model
 
TrainControl <- trainControl( method = "repeatedcv", number = 10, repeats = 4)
 
model<- train(y ~ ., data = x, method = "xgbLinear", trControl = TrainControl,verbose = FALSE)
 
OR 
 
model<- train(y ~ ., data = x, method = "xgbTree", trControl = TrainControl,verbose = FALSE)
 
predicted <- predict(model, x_test)

1. LightGBM

1、  Python代码

data = np.random.rand(500, 10) # 500 entities, each contains 10 features
label = np.random.randint(2, size=500) # binary target
 
train_data = lgb.Dataset(data, label=label)
test_data = train_data.create_valid('test.svm')
 
param = {'num_leaves':31, 'num_trees':100, 'objective':'binary'}
param['metric'] = 'auc'
 
num_round = 10
bst = lgb.train(param, train_data, num_round, valid_sets=[test_data])
 
bst.save_model('model.txt')
 
# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
ypred = bst.predict(data)

2、  R代码

library(RLightGBM)
data(example.binary)
#Parameters
 
num_iterations <- 100
config <- list(objective = "binary",  metric="binary_logloss,auc", learning_rate = 0.1, num_leaves = 63, tree_learner = "serial", feature_fraction = 0.8, bagging_freq = 5, bagging_fraction = 0.8, min_data_in_leaf = 50, min_sum_hessian_in_leaf = 5.0)
 
#Create data handle and booster
handle.data <- lgbm.data.create(x)
 
lgbm.data.setField(handle.data, "label", y)
 
handle.booster <- lgbm.booster.create(handle.data, lapply(config, as.character))
 
#Train for num_iterations iterations and eval every 5 steps
 
lgbm.booster.train(handle.booster, num_iterations, 5)
 
#Predict
pred <- lgbm.booster.predict(handle.booster, x.test)
 
#Test accuracy
sum(y.test == (y.pred > 0.5)) / length(y.test)
 
#Save model (can be loaded again via lgbm.booster.load(filename))
lgbm.booster.save(handle.booster, filename = "/tmp/model.txt")

require(caret)
require(RLightGBM)
data(iris)
 
model <-caretModel.LGBM()
 
fit <- train(Species ~ ., data = iris, method=model, verbosity = 0)
print(fit)
y.pred <- predict(fit, iris[,1:4])
 
library(Matrix)
model.sparse <- caretModel.LGBM.sparse()
 
#Generate a sparse matrix
mat <- Matrix(as.matrix(iris[,1:4]), sparse = T)
fit <- train(data.frame(idx = 1:nrow(iris)), iris$Species, method = model.sparse, matrix = mat, verbosity = 0)
print(fit)

1. CatBoost

1、  Python代码

import pandas as pd
import numpy as np
 
from catboost import CatBoostRegressor
 
#Read training and testing files
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
 
#Imputing missing values for both train and test
train.fillna(-999, inplace=True)
test.fillna(-999,inplace=True)
 
#Creating a training set for modeling and validation set to check model performance
X = train.drop(['Item_Outlet_Sales'], axis=1)
y = train.Item_Outlet_Sales
 
from sklearn.model_selection import train_test_split
 
X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.7, random_state=1234)
categorical_features_indices = np.where(X.dtypes != np.float)[0]
 
#importing library and building model
from catboost import CatBoostRegressormodel=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE')
 
model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)
 
submission = pd.DataFrame()
 
submission['Item_Identifier'] = test['Item_Identifier']
submission['Outlet_Identifier'] = test['Outlet_Identifier']
submission['Item_Outlet_Sales'] = model.predict(test)

2、  R代码

set.seed(1)
 
require(titanic)
 
require(caret)
 
require(catboost)
 
tt <- titanic::titanic_train[complete.cases(titanic::titanic_train),]
 
data <- as.data.frame(as.matrix(tt), stringsAsFactors = TRUE)
 
drop_columns = c("PassengerId", "Survived", "Name", "Ticket", "Cabin")
 
x <- data[,!(names(data) %in% drop_columns)]y <- data[,c("Survived")]
 
fit_control <- trainControl(method = "cv", number = 4,classProbs = TRUE)
 
grid <- expand.grid(depth = c(4, 6, 8),learning_rate = 0.1,iterations = 100, l2_leaf_reg = 1e-3,            rsm = 0.95, border_count = 64)
 
report <- train(x, as.factor(make.names(y)),method = catboost.caret,verbose = TRUE, preProc = NULL,tuneGrid = grid, trControl = fit_control)
 
print(report)
 
importance <- varImp(report, scale = FALSE)
 
print(importance)