我有 95 个数值变量和 5 个分类变量的数据。我的 Y 有 2 个值。我建立了一个决策树，我的准确率为 81.8%。然后我创建了 3 个新变量，如下所示。准确率提高到 84.3%
我想知道我是否可以创建任何其他新变量来提高准确性
我使用了决策树，因为它构建速度很快，并且可以指示新创建的变量是否有用。
如果您有任何想法，请告诉我
您可以使用 k-means 之类的方法对数据进行聚类，并将分配的聚类用作新功能。
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
data = load_iris()
X = pd.DataFrame(data['data'], columns=data['feature_names'])
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X.copy(), y, test_size=0.2)
km = KMeans(n_clusters=3)
km.fit(X_train)
training_dist = km.transform(X_train)
X_train['dist1'] = training_dist[:,0]
X_train['dist2'] = training_dist[:,1]
X_train['dist3'] = training_dist[:,2]
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
# new data
test_dist = km.transform(X_test)
X_test['dist1'] = test_dist[:,0]
X_test['dist2'] = test_dist[:,1]
X_test['dist3'] = test_dist[:,2]
clf.predict(X_test)