Python使用sklearn庫實現的各種分類算法簡單應用小結

更新時間：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庫實現的各種分類算法,結合實例形式分析了Python使用sklearn庫實現的KNN、SVM、LR、決策樹、隨機森林等算法實現技巧,需要的朋友可以參考下

本文實例講述了Python使用sklearn庫實現的各種分類算法簡單應用。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 分別為訓練數據集的數據和標簽，XX為測試數據
  model = KNeighborsClassifier(n_neighbors=10)#默認為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（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：一個是基于高斯分布求概率，一個是基于多項式分布求概率，一個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

更多關于Python相關內容感興趣的讀者可查看本站專題：《Python數據結構與算法教程》、《Python加密解密算法與技巧總結》、《Python編碼操作技巧總結》、《Python函數使用技巧總結》、《Python字符串操作技巧匯總》及《Python入門與進階經典教程》

希望本文所述對大家Python程序設計有所幫助。

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