Is it okay not to split the dataset into training and testing for text classification? (python code for stacking/ ensemble learning

@divisha wrote:

I found the following python code for Ensemble machine learning( stacking) on Github. It increased the accuracy of my classifiers but it is different from the other common methods of text classification which involves splitting of the data set into testing and training. Is it okay not to split the dataset into training and test dataset? What are the disadvantages if done so?
Here’s the code-

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.classifier import StackingClassifier
from sklearn import cross_validation
import numpy as np
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

def CalculateAccuracy(y_test,pred_label):
nnz = np.shape(y_test)[0] - np.count_nonzero(pred_label - y_test)
acc = 100*nnz/float(np.shape(y_test)[0])
return acc

clf1 = KNeighborsClassifier(n_neighbors=2)
clf2 = RandomForestClassifier(n_estimators = 2,random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()

clf1.fit(X, y)
clf2.fit(X, y)
clf3.fit(X, y)

f1 = clf1.predict(X)
acc1 = CalculateAccuracy(y, f1)
print("accuracy from KNN: "+str(acc1) )

f2 = clf2.predict(X)
acc2 = CalculateAccuracy(y, f2)
print("accuracy from Random Forest: "+str(acc2) )

f3 = clf3.predict(X)
acc3 = CalculateAccuracy(y, f3)
print("accuracy from Naive Bays: "+str(acc3) )

f = [f1,f2,f3]
f = np.transpose(f)

lr.fit(f, y)
final = lr.predict(f)

acc4 = CalculateAccuracy(y, final)
print("accuracy from Stacking: "+str(acc4) )

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