Python使用sklearn库实现的各种分类算法简单应用小结

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本文实例讲述了Python使用sklearn库实现的各种分类算法简单应用。分享给大家供大家参考,具体如下:

KNN

from sklearn.neighbors import KNeighborsClassifier
import numpy as np
def KNN(X,y,XX):#X,y 分别为训练数据集的数据和标签,XX为测试数据
  model = KNeighborsClassifier(n_neighbors=10)#默认为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

决策树(CART)

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

随机森林

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

朴素贝叶斯:一个是基于高斯分布求概率,一个是基于多项式分布求概率,一个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

希望本文所述对大家Python程序设计有所帮助。

以上就是Python使用sklearn库实现的各种分类算法简单应用小结。没人在乎你怎样在深夜里痛哭,也没人在乎你辗转反侧的要熬几个秋。外人只看结果,自己独撑过程。等我们都明白了这个道理,便不会再在人前矫情,四处诉说以求宽慰。更多关于Python使用sklearn库实现的各种分类算法简单应用小结请关注haodaima.com其它相关文章!

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