问【求助】python分析数据规律，如何提升准确性？

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris_data = load_iris()
X_train,X_test,y_train,y_test = train_test_split(\
    iris_data['data'],iris_data['target'],random_state=0)

# 做出预测
X_new = np.array([[29,4,19,4]])

import numpy
from sklearn.svm import SVC
svmClf = SVC(random_state=42)
svmClf.fit(X_train, y_train)
all_pre_and_get_score = svmClf.decision_function(X_new)
all_pre_and_get_score

# Softmax函数
def Softmax(s : list or numpy.ndarray):
    S = numpy.array([numpy.exp(i) for i in s])
    u = numpy.sum(S)
    for j in S:
        yield j/u
# 转换看下个数字出现的概率
for i in Softmax(all_pre_and_get_score):
    print(i)
    aa = numpy.array(i)
    bb = aa.tolist()
    m2=bb.copy()
    m2.remove(max(bb))
    m3=max(m2)
    m4=m2.copy()
    m4.remove(max(m2))
    m5=max(m4)
    print()
    print(max(i), '{}'.format(bb.index(max(bb))))
    print(m3,'{}'.format(bb.index(max(m2))))
    print(m5,'{}'.format(bb.index(max(m4))))