Python使用sklearn库实现的各种分类算法简单应用小结_Python

Python使用sklearn库实现的各种分类算法简单应用小结

2021-08-01 00:30Yeoman92 Python

这篇文章主要介绍了Python使用sklearn库实现的各种分类算法,结合实例形式分析了Python使用sklearn库实现的KNN、SVM、LR、决策树、随机森林等算法实现技巧,需要的朋友可以参考下

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

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									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

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									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

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									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

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									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）

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									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

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									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)

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									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。

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									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