请将svm、随机森林、catboost进行stacking

首先，我们需要训练三个不同的模型：SVM，随机森林和CatBoost。然后，我们将使用这些模型的预测结果作为训练数据来训练一个次级模型。

以下是一个示例代码：

from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np

# Load dataset
X, y = load_data()

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train SVM model
svm_model = SVC(kernel='rbf', C=1, gamma='auto')
svm_model.fit(X_train, y_train)

# Train Random Forest model
rf_model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
rf_model.fit(X_train, y_train)

# Train CatBoost model
cat_model = CatBoostClassifier(iterations=100, learning_rate=0.1, depth=6, random_seed=42)
cat_model.fit(X_train, y_train)

# Make predictions on test data for each model
svm_preds = svm_model.predict(X_test)
rf_preds = rf_model.predict(X_test)
cat_preds = cat_model.predict(X_test)

# Combine predictions into a single 2D array
preds = np.column_stack((svm_preds, rf_preds, cat_preds))

# Train a secondary model (Logistic Regression) on the predictions
from sklearn.linear_model import LogisticRegression
lr_model = LogisticRegression(random_state=42)
lr_model.fit(preds, y_test)

# Make predictions on test data using the stacked model
stacked_preds = lr_model.predict(preds)

# Calculate accuracy of stacked model
accuracy = accuracy_score(y_test, stacked_preds)
print("Stacked Model Accuracy: {:.2f}%".format(accuracy*100))

在这个示例中，我们首先加载和拆分数据集，然后训练三个不同的模型（SVM，随机森林和CatBoost）。接下来，我们使用每个模型对测试数据进行预测，并将预测结果组合成一个2D数组。然后，我们使用这些预测结果来训练一个次级模型（逻辑回归）。最后，我们使用次级模型对测试数据进行预测，并计算准确性。