I am dealing with a weird performance using SVC classifier in sklearn. I decided to use kfold cross validation in pima indians dataset. Since I wanted to try a SVC classifier I normalized the data using MinMaxScaler(feature_range=(0, 1)) to get features values between 0 and 1. But when I run the model I get 100% accuracy in each fold which obviously it is impossible. I looked for any error in the code but didn't come across with something strange. Here is my code. Any suggestion of this behaviour?
PD: I obviously load all needes libraries. I download the dataset from here https://gist.github.com/ktisha/c21e73a1bd1700294ef790c56c8aec1f and parse it to make things easier later on. Did I miss a step?
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
pima = pd.read_csv("pima dataset.txt",names = col_names)
X = pima[col_names].as_matrix()
y = pima.label.as_matrix()
scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# summarize transformed data
np.set_printoptions(precision=3)
#check transformations
print(rescaledX[0:5,:])
X_train, X_test, y_train, y_test = train_test_split(rescaledX,y, test_size = 0.2, random_state =42)
from sklearn.svm import SVC
import random
clf_1 = SVC(random_state = 42) #create a default model
clf_1.fit(X_train, y_train) #fitting the model
r_svc = [random.randrange(1,1000) for i in range(3)] #create a random seed for the 3 simulations.
scores_matrix_clf_1 = []
for i in r_svc:
kf = KFold(n_splits=10, shuffle = True, random_state = i)
kf.get_n_splits(X)
scores = cross_val_score(clf_1, X_train, y_train, cv=kf, n_jobs=-1, scoring = "accuracy")
print(' SCORES FOR EACH RANDOM THREE SEEDS',i)
print('-----------------------------SCORES----------------------------------------')
print(scores, scores.mean())
scores_matrix_clf_1.append(scores)
The output I am getting is this:
SCORES FOR EACH RANDOM THREE SEEDS 617
-----------------------------SCORES----------------------------------------
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] 1.0
SCORES FOR EACH RANDOM THREE SEEDS 764
-----------------------------SCORES----------------------------------------
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] 1.0
SCORES FOR EACH RANDOM THREE SEEDS 395
-----------------------------SCORES----------------------------------------
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] 1.0
