Simple application summary of various classification algorithms implemented by Python using sklearn library

This paper describes the simple application of various classification algorithms implemented by Python using sklearn library. Share it for your reference, as follows:

KNN

from sklearn.neighbors import KNeighborsClassifier
import numpy as np
def KNN(X,y,XX) : #X,y  They are the data and labels of the training data set, XX For test data 
  model = KNeighborsClassifier(n_neighbors=10)# Default to 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

Decision Tree (CART)

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

Random forest

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

Naive Bayes: One is based on Gaussian distribution, one is based on polynomial distribution, and one is based on Bernoulli distribution.

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

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