Question

我正在比较一些关于多分类问题的模型（梯度提升机，随机森林，逻辑回归，SVM，多层感知器和keras神经网络）。我在模型上使用了嵌套的交叉验证和网格搜索，并在我的实际数据和随机数据上运行了它们，以检查是否过拟合。但是，对于我发现的梯度提升机，无论我如何更改数据或模型参数，每次都能为随机数据提供100％的精度。我的代码中是否有某些原因可能导致这种情况？

这是我的代码：

dataset= pd.read_csv('data.csv')
data = dataset.drop(["gene"],1)
df = data.iloc[:,0:26]
df = df.fillna(0)
X = MinMaxScaler().fit_transform(df)

le = preprocessing.LabelEncoder()
encoded_value = le.fit_transform(["certain", "likely", "possible", "unlikely"])
Y = le.fit_transform(data["category"])

sm = SMOTE(random_state=100)
X_res, y_res = sm.fit_resample(X, Y)

seed = 7
logreg = LogisticRegression(penalty='l1', solver='liblinear',multi_class='auto')
LR_par= {'penalty':['l1'], 'C': [0.5, 1, 5, 10], 'max_iter':[100, 200, 500, 1000]}

rfc =RandomForestClassifier(n_estimators=500)
param_grid = {"max_depth": [3],
             "max_features": ["auto"],
              "min_samples_split": [2],
              "min_samples_leaf": [1],
              "bootstrap": [False],
              "criterion": ["entropy", "gini"]}


mlp = MLPClassifier(random_state=seed)
parameter_space = {'hidden_layer_sizes': [(50,50,50)],
     'activation': ['relu'],
     'solver': ['adam'],
     'max_iter': [10000],
     'alpha': [0.0001],
     'learning_rate': ['constant']}

gbm = GradientBoostingClassifier()
param = {"loss":["deviance"],
    "learning_rate": [0.001],
    "min_samples_split": [2],
    "min_samples_leaf": [1],
    "max_depth":[3],
    "max_features":["auto"],
    "criterion": ["friedman_mse"],
    "n_estimators":[50]
    }

svm = SVC(gamma="scale")
tuned_parameters = {'kernel':('linear', 'rbf'), 'C':(1,0.25,0.5,0.75)}

inner_cv = KFold(n_splits=10, shuffle=True, random_state=seed)

outer_cv = KFold(n_splits=10, shuffle=True, random_state=seed)


def baseline_model():

    model = Sequential()
    model.add(Dense(100, input_dim=X_res.shape[1], activation='relu')) #dense layers perform: output = activation(dot(input, kernel) + bias).
    model.add(Dropout(0.5))
    model.add(Dense(50, activation='relu')) #8 is the dim/ the number of hidden units (units are the kernel)
    model.add(Dense(4, activation='softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

models = []

models.append(('GBM', GridSearchCV(gbm, param, cv=inner_cv,iid=False, n_jobs=1)))
models.append(('RFC', GridSearchCV(rfc, param_grid, cv=inner_cv,iid=False, n_jobs=1)))
models.append(('LR', GridSearchCV(logreg, LR_par, cv=inner_cv, iid=False, n_jobs=1)))
models.append(('SVM', GridSearchCV(svm, tuned_parameters, cv=inner_cv, iid=False, n_jobs=1)))
models.append(('MLP', GridSearchCV(mlp, parameter_space, cv=inner_cv,iid=False, n_jobs=1)))
models.append(('Keras', KerasClassifier(build_fn=baseline_model, epochs=100, batch_size=50, verbose=0)))

results = []
names = []
scoring = 'accuracy'
X_train, X_test, Y_train, Y_test = train_test_split(X_res, y_res, test_size=0.2, random_state=0)


for name, model in models:
    nested_cv_results = model_selection.cross_val_score(model, X_res, y_res, cv=outer_cv, scoring=scoring)
    results.append(nested_cv_results)
    names.append(name)
    msg = "Nested CV Accuracy %s: %f (+/- %f )" % (name, nested_cv_results.mean()*100, nested_cv_results.std()*100)
    print(msg)
    model.fit(X_train, Y_train)
    print('Test set accuracy: {:.2f}'.format(model.score(X_test, Y_test)*100),  '%')

输出：

Nested CV Accuracy GBM: 90.952381 (+/- 2.776644 )
Test set accuracy: 90.48 %
Nested CV Accuracy RFC: 79.285714 (+/- 5.112122 )
Test set accuracy: 75.00 %
Nested CV Accuracy LR: 91.904762 (+/- 4.416009 )
Test set accuracy: 92.86 %
Nested CV Accuracy SVM: 94.285714 (+/- 3.563483 )
Test set accuracy: 96.43 %
Nested CV Accuracy MLP: 91.428571 (+/- 4.012452 )
Test set accuracy: 92.86 %

随机数据代码：

ran = np.random.randint(4, size=161)
random = np.random.normal(500, 100, size=(161,161))
rand = np.column_stack((random, ran))
print(rand.shape)
X1 = rand[:161]
Y1 = rand[:,-1]
print("Random data counts of label '1': {}".format(sum(ran==1)))
print("Random data counts of label '0': {}".format(sum(ran==0)))
print("Random data counts of label '2': {}".format(sum(ran==2)))
print("Random data counts of label '3': {}".format(sum(ran==3)))

for name, model in models:
    cv_results = model_selection.cross_val_score(model, X1, Y1,  cv=outer_cv, scoring=scoring)
    names.append(name)
    msg = "Random data CV %s: %f (+/- %f)" % (name, cv_results.mean()*100, cv_results.std()*100)
    print(msg)

随机数据输出：

Random data CV GBM: 100.000000 (+/- 0.000000)
Random data CV RFC: 62.941176 (+/- 15.306485)
Random data CV LR: 23.566176 (+/- 6.546699)
Random data CV SVM: 22.352941 (+/- 6.331220)
Random data CV MLP: 23.639706 (+/- 7.371392)
Random data CV Keras: 22.352941 (+/- 8.896451)

无论我是否减少要素数量，更改网格搜索中的参数，此梯度提升分类器（GBM）均为100％（我确实输入了多个参数，但是这对我来说可能会运行数小时而没有结果，所以我离开了该问题暂时存在），并且如果我尝试使用二进制分类数据也是如此。

随机森林（RFC）也达到了62％，我在做错什么吗？

我正在使用的数据主要是二进制功能，例如如下所示（并预测类别列）：

gene   Tissue    Druggable Eigenvalue CADDvalue Catalogpresence   Category
ACE      1           1         1          0           1            Certain
ABO      1           0         0          0           0            Likely
TP53     1           1         0          0           0            Possible

任何指导将不胜感激。

Answer 1

通常，您可以使用一些参数来减少过度拟合。从概念上最容易理解的是增加min_samples_split和min_samples_leaf。为这些值设置更高的值将使模型无法记住如何正确识别单个数据或非常小的数据组。对于大型数据集（约100万行），我将这些值设置为大约50（如果不更高）。您可以进行网格搜索以找到适合您的特定数据的值。

您还可以使用子样本来减少过拟合和max_features。这些参数基本上不会让您的模型查看一些数据，从而阻止其记忆。

如何防止梯度增压机过度安装？

1 个答案: