Support vector machine overfitting my data

Question

I am trying to make predictions for the iris dataset. I have decided to use svms for this purpose. But, it gives me an accuracy 1.0. Is it a case of overfitting or is it because the model is very good? Here is my code.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
svm_model = svm.SVC(kernel='linear', C=1,gamma='auto')
svm_model.fit(X_train,y_train)
predictions = svm_model.predict(X_test)
accuracy_score(predictions, y_test)

Here, accuracy_score returns a value of 1. Please help me. I am a beginner in machine learning.

Answer 1

You can try cross validation:

Example:

from sklearn.model_selection import LeaveOneOut
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

#load iris data
iris = datasets.load_iris()
X = iris.data
Y = iris.target

#build the model
svm_model = SVC( kernel ='linear', C = 1, gamma = 'auto',random_state = 0 )

#create the Cross validation object
loo = LeaveOneOut()

#calculate cross validated (leave one out) accuracy score
scores = cross_val_score(svm_model, X,Y, cv = loo, scoring='accuracy')

print( scores.mean() )

Result (the mean accuracy of the 150 folds since we used leave-one-out):

0.97999999999999998

Bottom line:

Cross validation (especially LeaveOneOut) is a good way to avoid overfitting and to get robust results.