Is there a way for me to view the classes of particular instances in binary classified dataset using scikit learn
Is there a way for me to view the classes of particular instances in a binary classified dataset(classes are 0 or 1) using the scikit learn library. I have a program that performs knn on a dataset and inserts the model built into a database. I want to view instances in the the data that are classified as 0 or 1. This is the link to the dataset I am using https://drive.google.com/open?id=0B9cQtdthFqv3YVNBNk1peVltZkk from sklearn import neighbors,datasets,preprocessing from sklearn.cross_validation import train_test_split import urllib.request from sklearn.metrics import accuracy_score import pandas as pd import matplotlib.pyplot as plt from sklearn import model_selection from sklearn.metrics import precision_recall_fscore_support as score from sklearn.externals import joblib from sklearn.model_selection import cross_val_score from pandas import read_csv from sklearn.ensemble import ExtraTreesClassifier import pandas as pd import numpy as np import sys import pickle import mysql.connector as mc def doSupervised(username,fileid,filename): try: connection = mc.connect (host = "localhost", user = "**", passwd = "**", db = "**") except mc.Error as e: print("Error %d: %s" % (e.args, e.args)) sys.exit(1) cursor = connection.cursor() # load data dataframe = pd.DataFrame.from_csv(filename) X= np.array(dataframe) y = np.array(dataframe['Class']) # fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit_transform(X, y) # display the relative importance of each attribute #print(model.feature_importances_) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) clf = neighbors.KNeighborsClassifier(algorithm='auto') #fit the data to the model clf.fit(X_train, y_train) y_pred = clf.predict(X_test) #print (y_pred) algorithmType="Supervised" accuracy =accuracy_score(y_test, y_pred ) print(accuracy) #save classifier to a file name="supervisedmodel.pkl" data = joblib.dump(clf, name) #name = 'supervisedmodel.sav' #data = pickle.dump(clf, open(name, 'wb')) sql_command = "INSERT INTO machinelearningmodel (username, fileID, name,data,accuracy,algorithmType ) VALUES (%s, %s, %s, %s, %s, %s)", (username, str(fileid), name, str(data), str(accuracy),algorithmType) cursor.execute(*sql_command) #print(sql_command) cursor.close() connection.commit() connection.close() if __name__ == "__main__": doSupervised(sys.argv,sys.argv,sys.argv)
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