Is there any possibility to vary the accuracy of same data set in matlab and jupyter notebook by using python code ?
For same data set, at first I applied it in matlab and get 96% accuracy for decision tree method, then I apply that same data set in jupyter notebook by using python code where I get 53% accuracy for C4.5 (decision tree) by using k-fold cross validation.
I didn't understand where's the problem for getting different accuracy for same dataset and same method.
My procedure in python code is given below:
import pandas as pd import numpy as np from sklearn import tree from sklearn.model_selection import KFold train=pd.read_csv('E://New.csv') train.head()
# define X and y feature_cols = ['Past','Family_History','Current','current or previous workplace','diagnosed with a mental health condition by a medical professional?','do you feel that it interferes with your work when being treated effectively?','Gender'] X = train[feature_cols] # y is a vector, hence we use dot to access 'label' y = train['Diagonised condition'] kfold = KFold(n_splits=10,random_state=None) model = tree.DecisionTreeClassifier(criterion='gini') results = cross_val_score(model, X, y, cv=kfold,scoring = 'accuracy') result = results.mean()*100 std = results.std()*100 print (result)