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千鋒扣丁學(xué)堂Python培訓(xùn)之使用sklearn庫實(shí)現(xiàn)各種分類算法

2019-07-04 13:44:06 2420瀏覽

今天千鋒扣丁學(xué)堂Python培訓(xùn)老師給大家分享一篇關(guān)于介紹了Python使用sklearn庫實(shí)現(xiàn)的各種分類算法,結(jié)合實(shí)例形式分析了Python使用sklearn庫實(shí)現(xiàn)的KNN、SVM、LR、決策樹、隨機(jī)森林等算法實(shí)現(xiàn)技巧，下面我們一起來看一下吧。

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（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

以上就是關(guān)于千鋒扣丁學(xué)堂Python培訓(xùn)之使用sklearn庫實(shí)現(xiàn)各種分類算法的全部內(nèi)容，希望本文所述對(duì)大家Python程序設(shè)計(jì)有所幫助，想要了解更多關(guān)于Python開發(fā)方面內(nèi)容的小伙伴，請(qǐng)關(guān)注扣丁學(xué)堂Python培訓(xùn)官網(wǎng)、微信等平臺(tái)，扣丁學(xué)堂IT職業(yè)在線學(xué)習(xí)教育有專業(yè)的Python講師為您指導(dǎo)，此外扣丁學(xué)堂老師精心推出的Python視頻教程定能讓你快速掌握Python從入門到精通開發(fā)實(shí)戰(zhàn)技能。扣丁學(xué)堂Python技術(shù)交流群：279521237。

                          

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