问如何改进knn模型？
EN

Stack Overflow用户

提问于 2022-12-03 04:44:19

回答 2查看 23关注 0票数 -1

我建立了一个knn分类模型。不幸的是，我的模型有超过80%的准确性，我想得到一个更好的结果。我能要些小费吗？也许我用了太多的预测器？

我的数据= https://www.openml.org/search?type=data&sort=runs&id=53&status=active

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
from sklearn.model_selection import GridSearchCV

heart_disease = pd.read_csv('heart_disease.csv', sep=';', decimal=',')
y = heart_disease['heart_disease']
X = heart_disease.drop(["heart_disease"], axis=1)

correlation_matrix = heart_disease.corr()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)

scaler = MinMaxScaler(feature_range=(-1,1))

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

knn_3 = KNeighborsClassifier(3, n_jobs = -1)
knn_3.fit(X_train, y_train)
y_train_pred = knn_3.predict(X_train)
labels = ['0', '1']

print('Training set')
print(pd.DataFrame(confusion_matrix(y_train, y_train_pred), index = labels, columns = labels))
print(accuracy_score(y_train, y_train_pred))
print(f1_score(y_train, y_train_pred))

y_test_pred = knn_3.predict(X_test)

print('Test set')
print(pd.DataFrame(confusion_matrix(y_test, y_test_pred), index = labels, columns = labels))
print(accuracy_score(y_test, y_test_pred))
print(f1_score(y_test, y_test_pred))

hyperparameters = {'n_neighbors' : range(1, 15), 'weights': ['uniform','distance']}

knn_best = GridSearchCV(KNeighborsClassifier(), hyperparameters, n_jobs = -1, error_score = 'raise')
knn_best.fit(X_train,y_train)
knn_best.best_params_

y_train_pred_best = knn_best.predict(X_train)
y_test_pred_best = knn_best.predict(X_test)

print('Training set')
print(pd.DataFrame(confusion_matrix(y_train, y_train_pred_best), index = labels, columns = labels))
print(accuracy_score(y_train, y_train_pred_best))
print(f1_score(y_train, y_train_pred_best))

print('Test set')
print(pd.DataFrame(confusion_matrix(y_test, y_test_pred_best), index = labels, columns = labels))
print(accuracy_score(y_test, y_test_pred_best))
print(f1_score(y_test, y_test_pred_best))

python

machine-learning

scikit-learn

knn

回答 2

Stack Overflow用户

发布于 2022-12-03 04:53:57

有几件事你可以尝试提高你的KNN模型的准确性。

首先，您可以尝试调优模型的超参数，例如要考虑的最近邻居的数目，或者用来度量点间相似性的距离度量。

要调优KNN模型的超参数，可以使用诸如cross-validation、、网格搜索、等技术来尝试不同的超参数组合，并找到最适合您的数据的组合。

您还可以尝试预处理您的数据，使其更适合于KNN。例如，您可以尝试使用主成分分析 (PCA)等技术来降低数据的维度。这可以帮助消除数据中的冗余，减少维数，这可以使KNN更容易找到最近的邻居。

此外，您可以尝试完全使用不同的分类算法，例如logistic回归或决策树。这些算法可能更适合您的数据，并可能产生比KNN更好的结果。

您可以尝试的另一件事是使用集成方法，例如打包或增强，将多个KNN模型组合起来，并可能提高它们的准确性。集成方法可以有效地减少过度拟合和提高模型的通用性。

票数 2

Stack Overflow用户

发布于 2022-12-03 05:10:35

这只是答案的一小部分，以便为k_neighbors找到最佳的数字。

errlist = [] #an error list to append
for i in range(1,40): #from 0-40 numbers to use in k_neighbors
    knn_i = KNeighborsClassifier(k_neighbors=i)
    knn_i.fit(X_train,y_train)
    errlist.append(np.mean(knn_i.predict(X_test)!=y_test)) # append the mean of failed-predict numbers