I want to find the raw data which are classified successfully and which are not classified after Multinomial Nieves Bayes Classification algorithm is applied. For instance I got the accuracy as 88% after applying Multinomail Naives Bayes classification. I want to know the 12% of data which are not classified and also 88% of the data that is classified. Thanks in advance
My data set:
+----------------------+------------+
| Details | Category |
+----------------------+------------+
| Any raw text1 | cat1 |
+----------------------+------------+
| any raw text2 | cat1 |
+----------------------+------------+
| any raw text5 | cat2 |
+----------------------+------------+
| any raw text7 | cat1 |
+----------------------+------------+
| any raw text8 | cat2 |
+----------------------+------------+
| Any raw text4 | cat4 |
+----------------------+------------+
| any raw text5 | cat4 |
+----------------------+------------+
| any raw text6 | cat3 |
+----------------------+------------+
My code:
import pandas as pd
import numpy as np
import scipy as sp
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
data= pd.read_csv('mydat.xls', delimiter='\t',usecols=
['Details','Category'],encoding='utf-8')
target_one=data['Category']
target_list=data['Category'].unique()
x_train, x_test, y_train, y_test = train_test_split(data.Details,
data.Category, random_state=42)
vect = CountVectorizer(ngram_range=(1,2))
#converting traning features into numeric vector
X_train = vect.fit_transform(x_train.values.astype('U'))
#converting training labels into numeric vector
X_test = vect.transform(x_test.values.astype('U'))
# start = time.clock()
mnb = MultinomialNB(alpha =0.13)
mnb.fit(X_train,y_train)
result= mnb.predict(X_test)
# mnb.predict_proba(x_test)[0:10,1]
accuracy_score(result,y_test)
Just iterate over your Data:
for i in range(len(y_test)):
if result[i] == y_test[i]:
print("CORRECT: ", X_test[i])
else
print("INCORRECT: ", X_test[i])
you can add them to two different lists or only print the id or do whatever you want instead.
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