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Python使用sklearn庫(kù)實(shí)現(xiàn)的各種分類(lèi)算法簡(jiǎn)單應(yīng)用小結(jié)

更新時(shí)間：2019年07月04日 11:08:35 作者：Yeoman92

這篇文章主要介紹了Python使用sklearn庫(kù)實(shí)現(xiàn)的各種分類(lèi)算法,結(jié)合實(shí)例形式分析了Python使用sklearn庫(kù)實(shí)現(xiàn)的KNN、SVM、LR、決策樹(shù)、隨機(jī)森林等算法實(shí)現(xiàn)技巧,需要的朋友可以參考下

本文實(shí)例講述了Python使用sklearn庫(kù)實(shí)現(xiàn)的各種分類(lèi)算法簡(jiǎn)單應(yīng)用。分享給大家供大家參考，具體如下：

KNN

from sklearn.neighbors import KNeighborsClassifier
import numpy as np
def KNN(X,y,XX)：#X,y 分別為訓(xùn)練數(shù)據(jù)集的數(shù)據(jù)和標(biāo)簽，XX為測(cè)試數(shù)據(jù)
  model = KNeighborsClassifier(n_neighbors=10)#默認(rèn)為5
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted

SVM

from sklearn.svm import SVC
def SVM(X,y,XX):
  model = SVC(c=5.0)
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted

SVM Classifier using cross validation

def svm_cross_validation(train_x, train_y):
  from sklearn.grid_search import GridSearchCV
  from sklearn.svm import SVC
  model = SVC(kernel='rbf', probability=True)
  param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]}
  grid_search = GridSearchCV(model, param_grid, n_jobs = 1, verbose=1)
  grid_search.fit(train_x, train_y)
  best_parameters = grid_search.best_estimator_.get_params()
  for para, val in list(best_parameters.items()):
    print(para, val)
  model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True)
  model.fit(train_x, train_y)
  return model

LR

from sklearn.linear_model import LogisticRegression
def LR(X,y，XX):
  model = LogisticRegression()
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted

決策樹(shù)（CART）

from sklearn.tree import DecisionTreeClassifier
def CTRA(X,y,XX):
  model = DecisionTreeClassifier()
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted

隨機(jī)森林

from sklearn.ensemble import RandomForestClassifier
def CTRA(X,y,XX):
  model = RandomForestClassifier()
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted

GBDT(Gradient Boosting Decision Tree)

from sklearn.ensemble import GradientBoostingClassifier
def CTRA(X,y,XX):
  model = GradientBoostingClassifier()
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted

樸素貝葉斯：一個(gè)是基于高斯分布求概率，一個(gè)是基于多項(xiàng)式分布求概率，一個(gè)是基于伯努利分布求概率。

from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import BernoulliNB
def GNB(X,y,XX):
  model =GaussianNB()
  model.fit(X,y)
  predicted = model.predict(XX)
  return predicted
def MNB(X,y,XX):
  model = MultinomialNB()
  model.fit(X,y)
  predicted = model.predict(XX
  return predicted
def BNB(X,y,XX):
  model = BernoulliNB()
  model.fit(X,y)
  predicted = model.predict(XX
  return predicted

更多關(guān)于Python相關(guān)內(nèi)容感興趣的讀者可查看本站專(zhuān)題：《Python數(shù)據(jù)結(jié)構(gòu)與算法教程》、《Python加密解密算法與技巧總結(jié)》、《Python編碼操作技巧總結(jié)》、《Python函數(shù)使用技巧總結(jié)》、《Python字符串操作技巧匯總》及《Python入門(mén)與進(jìn)階經(jīng)典教程》

希望本文所述對(duì)大家Python程序設(shè)計(jì)有所幫助。

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