kneighborsclassifier

after entering film OneFil1 #sample characteristics of a movie2train=film[['Action Lens','Kissing Lens']] 3 #The sample label, which is the label to be predicted, is here to predict what category of movies The new data belongs to4target=film['Movie Category'] 5 #Create a machine learning model that needs to be imported6 fromSklearn.neighborsImportKneighborsclassifier7 #create objects, where the data is discrete, so use Kneighborsclassifier,8knn=

Recent Neighborhood Classification Concept explainedWe are using the neighbors in the Scikit-learn library. Kneighborsclassifier to implement KNN.fromimport neighborsneighbors.KNeighborsClassifier(n_neighbors=5, weights=‘uniform‘, algorithm=‘auto‘, leaf_size=30,p=2, metric=’minkowski’, metric_params=None, n_jobs=1)N_neighbors is used to determine the K value in most voting rules, that is, to select the most general range of K values around a pointWeig

Import Pylab as PL import NumPy as NP from sklearn.neighbors import kneighborsclassifier from Sklearn.metrics Import class Ification_report from sklearn.cross_validation import Train_test_split,stratifiedkfold,cross_val_score from Sklearn.decomposition Import PCA from sklearn.feature_selection import rfecv from SKLEARN.SVM import SVC import sklearn.pr Eprocessing as pp def dsplit (train_init,target_init): Train,test,train_target,test_target = Train_te

not interesting to summarize the performance of the training set you just learned in the model. Let's look at how the test set behaves, because it gives you a more intuitive impression of the model. Try using different K values: From Sklearn. NeighborsImport kneighborsclassifierfrom Sklearn Import Metrics knn99 = kneighborsclassifier (n_neighbors = About) knn99. Fit(Xtrain, ytrain) yPredK99 = knn99. Predict(XTest) Print"Overall Error of k=99 Mod

Import class library 1 import numpy as np 2 from sklearn.neighbors import KNeighborsClassifier 3 from sklearn.model_selection import train_test_split 4 from sklearn.preprocessing import StandardScaler 5 from sklearn.linear_model import LinearRegression 6 from sklearn.metrics import r2_score 7 from sklearn.datasets import load_iris 8 import matplotlib.pyplot as plt 9 import pandas as pd10 import seaborn as sns # Entropy gain # greater entropy, great

First, choose the correct model basis verification method From sklearn.datasets Import Load_iris # Iris DataSet from sklearn.model_selection import train_test_split # split data Module From sklearn.neighbors import Kneighborsclassifier # k nearest neighbor (Knn,k-nearestneighbor) classification algorithm #加载iris数据集 iris = Load_ Iris () x = iris.data y = iris.target #分割数据并 x_train, X_test, y_train, y_test = Train_test_split (X, Y, ran dom_state=4) #

Import pandas as PD import NumPy as NP sklearn.preprocessing import imputer# importing data preprocessing module processing raw data from Sklearn.model_selec tion import train_test_split# importing modules from Sklearn.metrics Import to automatically generate training sets and test sets classification_report# importing forecast results evaluation module from Sklearn.neighbors Import kneighborsclassifier#knn nearest neighbor algorithm from Sklearn.tree

法From sklearn.discriminant_analysis import lineardiscriminantanalysis #线性判别分析From sklearn.discriminant_analysis import quadraticdiscriminantanalysis #二次判别分析From Sklearn.tree import Decisiontreeregressor #决策树回归From Sklearn.tree import Decisiontreeclassifier #决策树分类From sklearn.neighbors import Kneighborsregressor #KNN回归From sklearn.neighbors import Kneighborsclassifier #KNN分类From Sklearn.naive_bayes import GAUSSIANNB #贝叶斯分类器From SKLEARN.SVM import SVR #

= kneighborsclassifier () model.fit (x, y)print== model.predict (x) Print(metrics.classification_report (expected, predicted))print( Metrics.confusion_matrix (expected, predicted))Results: kneighborsclassifier (Algorithm= " auto , leaf_size=30, Metric=" minkowski " , Metric_params =non E, N_jobs=1, n_neighbors=5, P=2 =" uniform ) precision Recall F1 -score support 0.0 0.82 0.90 0.86 1.0 0.

Python Machine Learning Library sciki-earn practice, pythonsciki-earn Use Anaconda's spyder: Create train_test.py #!usr/bin/env python #-*- coding: utf-8 -*- import sys import os import time from sklearn import metrics import numpy as np import cPickle as pickle reload(sys) sys.setdefaultencoding('utf8') # Multinomial Naive Bayes Classifier def naive_bayes_classifier(train_x, train_y): from sklearn.naive_bayes import MultinomialNB model = MultinomialNB(alpha=0.01)

1 #-*-coding:utf-8-*-2 """3 Created on Thu June 17:16:19 20184 5 @author: Zhen6 """7 fromSklearn.model_selectionImportTrain_test_split8 ImportMglearn9 ImportMatplotlib.pyplot as PltTenX, y =Mglearn.datasets.make_forge () One X_train, X_test, y_train, y_test = Train_test_split (x, Y, random_state=0) # Generate training and test set data A - fromSklearn.neighborsImportKneighborsclassifierCLF = Kneighborsclassifier (n_neighbors=3) # call K nearest Nei

neighbors in KNN as parameters. Step 3, extract the image features and write to the array. We use the Cv2.imread function to read images and classify them according to the normalized image names. Then run the 2 functions mentioned in step 1, get 2 image features and write to the array respectively. Step 4, use the function Train_test_split to split the dataset. 85% of the data as a training set, 15% of the data as a test set. Step 5, use the KNN,SVM and BP neural network method to evaluate the

the experimental analysis. In addition, we set the number of neighbors in KNN as parameters. Step 3, extract the image features and write to the array. We use the Cv2.imread function to read images and classify them according to the normalized image names. Then run the 2 functions mentioned in step 1, get 2 image features and write to the array respectively. Step 4, use the function Train_test_split to split the dataset. 85% of the data as a training set, 15% of the data as a test set. Step 5,

Baggingclassifier, with the base model of user input and the method of dividing subsets as parameters. where Max_samples and max_features control the size of the subset, and Bootstrap and bootstrap_features control whether the data samples and attributes are replaced. Oob_score=true makes it possible to use existing data to classify samples when estimating. The following example shows the integration of the kneighborclassifier estimate using the bagging method, with the training sample partitio

): # This function saves the result as a csv file named after csvName When processing data, we obtain the feature of the training sample, the label of the training sample, and the feature of the test sample from the train.csv1_test.csv file. We use trainData, trainLabel, and testData in the program. (2) Call The kNN algorithm in scikit-learn. # Call The knn algorithm package of scikit from sklearn. neighbors import into def knnClassify (trainData, trainLabel, testData): knnClf = encrypt () # d

import kneighborsclassifier Print ' *************************\nknn\n************************* ' KNNCLF = Kneighborsclassifier ()#default with k=5 Knnclf.fit (Fea_train,newsgroup_train.target) pred = Knnclf.predict (fea_test); Calculate_result (newsgroups_test.target,pred); 3.3 SVM:[CPP]View Plaincopy ###################################################### #SVM Classifie

90Unknown Task Description: Define the movie type by the number of fights and kisses to call Python's Sklearn module solver1.ImportNumPy as NP 2. fromSklearnImportNeighbors 3. KNN = neighbors. Kneighborsclassifier ()#get KNN classifier 4. Data = Np.array ([[3,104],[2,100],[1,81],[101,10],[99,5],[98,2]]) # Description: First, use 1 and 2 of the array in the labels array to represent romance and Aciton, because Sklearn does not accept the character arr

Picklereload (SYS) sys.setdefaultencoding (' UTF8 ') # multinomial Naive Bayes classifierdef Naive_bayes_ Classifier (train_x, train_y): From sklearn.naive_bayes import MULTINOMIALNB model = MULTINOMIALNB (alpha=0.01) mod El.fit (train_x, train_y) return model# KNN classifierdef knn_classifier (train_x, train_y): From Sklearn.neighbors im Port Kneighborsclassifier model = Kneighborsclassifier () model.fit