sklearn中的分类方法

### Multinomial Naive Bayes Classifier    
from sklearn.naive_bayes import MultinomialNB

clf = MultinomialNB(alpha=0.01)
clf.fit(train_x, train_y)


### KNN Classifier    
from sklearn.neighbors import KNeighborsClassifier

clf = KNeighborsClassifier()
clf.fit(train_x, train_y)


### Logistic Regression Classifier    
from sklearn.linear_model import LogisticRegression

clf = LogisticRegression(penalty='l2')
clf.fit(train_x, train_y)


### Random Forest Classifier    
from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=8)
clf.fit(train_x, train_y)


### Decision Tree Classifier    
from sklearn import tree

clf = tree.DecisionTreeClassifier()
clf.fit(train_x, train_y)


### GBDT(Gradient Boosting Decision Tree) Classifier    
from sklearn.ensemble import GradientBoostingClassifier

clf = GradientBoostingClassifier(n_estimators=200)
clf.fit(train_x, train_y)


### SVM Classifier    
from sklearn.svm import SVC

clf = SVC(kernel='rbf', probability=True)
clf.fit(train_x, train_y)

关于朴素贝叶斯中multinomialNB，gaussianNB，BernouliNB的解释，见：https://blog.csdn.net/brucewong0516/article/details/78798359

关于随机森林中参数的解释，见：
https://blog.csdn.net/w952470866/article/details/78987265

关于核函数的解释，见：
常用的核函数有：线性核函数，多项式核函数，径向基核函数，Sigmoid核函数和复合核函数，傅立叶级数核，B样条核函数和张量积核函数等https://baike.baidu.com/item/核函数/4693132?fr=aladdin

kernel: str参数 默认为‘rbf’, 算法中采用的核函数类型，可选参数有：‘linear’:线性核函数, ‘poly’：多项式核函数 , ‘rbf’：径像核函数/高斯核, ‘sigmod’: sigmod核函数, ‘precomputed’:核矩阵