-1

I create a program that predict digits from in a dataset. I want when it predict data their should be two cases if it predict right then data should added automatically in dataset otherwise it takes right answer throw user and insert to dataset.

code

import numpy as np
import pandas as pd
import matplotlib.pyplot as pt
from sklearn.tree import DecisionTreeClassifier
data = pd.read_csv("train.csv").values
clf = DecisionTreeClassifier()
xtrain = data[0:21000,1:]
train_label=data[0:21000,0]
clf.fit(xtrain,train_label)
xtest = data[21000: ,1:]
actual_label=data[21000:,0]
d = xtest[9]
d.shape = (28,28)
pt.imshow(d,cmap='gray')
print(clf.predict([xtest[9]]))
pt.show()
Community
  • 1
  • 1
Ajay Kumar Joshi
  • 11
  • 3

1 Answers1

0

I'm not sure I'm following your question, but if you want to distinguish between good and wrong predictions and take different ways, you should specific do that.

predictions = clf.predict(xtest)
good_predictions = xtest[pd.Series(predictions == actual_label)]
bad_predictions = xtest[pd.Series(predictions != actual_label)]

So, in good_predictions will be all the rows in xtest that where predicted right.

Julian Gutierrez Ostrovsky
  • 1