线性回归

#Import Library

#Import other necessary libraries like pandas,

#numpy...

from sklearn import linear_model

#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

#Create linear regression objectlinear = linear_model.LinearRegression()

#Train the model using the training sets and #check scorelinear.fit(x_train, y_train) linear.score(x_train, y_train)

#Equation coefficient and Intercept print('Coefficient: \n', linear.coef_) print('Intercept: \n', linear.intercept_) #Predict Output

predicted= linear.predict(x_test)

#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)

逻辑回归

#Import Library

from sklearn.linear_model import LogisticRegression

#Assumed you have, X (predictor) and Y (target)

#for training data set and x_test(predictor)

#of test_dataset

#Create logistic regression object

model = LogisticRegression()

#Train the model using the training sets

#and check score

model.fit(X, y)

model.score(X, y)

#Equation coefficient and Intercept

print('Coefficient: \n', model.coef_)

print('Intercept: \n', model.intercept_)

#Predict Output

predicted= model.predict(x_test)

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 Outputpredicted= predict(logistic,x_test)

决

策

树

#Import Library

#Import other necessary libraries like pandas, numpy... from sklearn import tree

#Assumed you have, X (predictor) and Y (target) for

#training data set and x_test(predictor) of #test_dataset

#Create tree objectmodel = tree.DecisionTreeClassifier(criterion='gini') #for classification, here you can change the #algorithm as gini or entropy (information gain) by

#default it is gin

#model = tree.DecisionTreeRegressor() for

#regression

#Train the model using the training sets and check #score

model.fit(X, y)

model.score(X, y)

#Predict Outputpredicted= model.predict(x_test)

#Import Library

library(rpart)

x <-cbind(x_train,y_train)

#grow tree

fit <- rpart(y_train ~ ., data = x,method="class") summary(fit)

#Predict Outputpredicted= predict(fit,x_test)

支持

向量机

#Import Library

from sklearn import svm

#Assumed you have, X (predictor) and Y (target) for #training data set and x_test(predictor) of test_dataset

#Create SVM classification objectmodel = svm.svc()

#there are various options associatedwith it, this is simple for classification.

#Train the model using the training sets and check #score

model.fit(X, y)

model.score(X, y)

#Predict Outputpredicted= model.predict(x_test)

#Import Library

library(e1071)

x <- cbind(x_train,y_train) #Fitting model

fit <-svm(y_train ~ ., data = x) summary(fit)

#Predict Outputpredicted= predict(fit,x_test)

贝叶斯算法

#Import Libraryfrom sklearn.naive_bayes import GaussianNB

#Assumed you have, X (predictor) and Y (target) for

#training data set and x_test(predictor) of test_dataset

#Create SVM classification object model = GaussianNB()

#there is other distribution for multinomial classes like Bernoulli Naive Bayes

#Train the model using the training sets and check

#scoremodel.fit(X, y)

#Predict Outputpredicted= model.predict(x_test)

#Import Librarylibrary(e1071)

x <- cbind(x_train,y_train)#Fitting model

fit <-naiveBayes(y_train ~ ., data = x) summary(fit)

#Predict Outputpredicted= predict(fit,x_test)

k-近邻算法析

#Import Library

from sklearn.neighbors import KNeighborsClassifier

#Assumed you have, X (predictor) and Y (target) for

#training data set and x_test(predictor) of test_dataset

#Create KNeighbors classifier object model KNeighborsClassifier(n_neighbors=6)

#default value for n_neighbors is 5

#Train the model using the training sets and check score model.fit(X, y)

#Predict Outputpredicted= model.predict(x_test)

#Import Librarylibrary(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)

硬聚类算法

#Import Library

from sklearn.cluster import KMeans

#Assumed you have, X (attributes) for training data set

#and x_test(attributes) of test_dataset

#Create KNeighbors classifier object model

k_means = KMeans(n_clusters=3, random_state=0)

#Train the model using the training sets and check score model.fit(X)

#Predict Outputpredicted= model.predict(x_test)

#Import Library

library(cluster)

fit <- kmeans(X, 3)

#5 cluster solution

随机森林算法

#Import Libraryfrom sklearn.ensemble import RandomForestClassifier

#Assumed you have, X (predictor) and Y (target) for

#training data set and x_test(predictor) of test_dataset

#Create Random Forest objectmodel= RandomForestClassifier()

#Train the model using the training sets and check score model.fit(X, y)

#Predict Outputpredicted= model.predict(x_test)

#Import Library

library(randomForest)

x <- cbind(x_train,y_train)

#Fitting model

fit <- randomForest(Species ~ ., x,ntree=500) summary(fit)

#Predict Outputpredicted= predict(fit,x_test)

降维算法

#Import Library

from sklearn import decomposition

#Assumed you have training and test data set as train and

#test

#Create PCA object pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)

#For Factor analysis

#fa= decomposition.FactorAnalysis()

#Reduced the dimension of training dataset using PCA train_reduced = pca.fit_transform(train)

#Reduced the dimension of test datasettest_reduced = pca.transform(test)

#Import Library

library(stats)

pca <- princomp(train, cor = TRUE)

train_reduced <- predict(pca,train)

test_reduced <- predict(pca,test)

GB

D

T

#Import Library

from sklearn.ensemble import GradientBoostingClassifier

#Assumed you have, X (predictor) and Y (target) for

#training data set and x_test(predictor) of test_dataset

#Create Gradient Boosting Classifier object

model= GradientBoostingClassifier(n_estimators=100, \ learning_rate=1.0, max_depth=1, random_state=0)

#Train the model using the training sets and check score model.fit(X, y)

#Predict Output

predicted= model.predict(x_test)

#Import Library

library(caret)

x <- cbind(x_train,y_train)

#Fitting modelfitControl <- 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]