Python uses the Bagging Ensemble learning of Scikit-learn

Preparation work: 1. Install Python: The first step is to install Python. It is recommended to use Python version 3.7 and above. 2. Install Scikit learn: Scikit learn is an open-source Python machine learning library that provides implementations of various machine learning algorithms. You can install Scikit learn using the following command: pip install scikit-learn 3. Download Dataset: The Iris Dataset, which comes with Scikit-learn, was used in this actual battle. Dataset introduction: The Iris dataset is a very classic classification problem dataset. It contains measurement data from three different varieties of iris flowers: Iris setosa, Iris versicolor, and Iris virginica. Each sample has 4 characteristics: calyx length, calyx width, petal length, and petal width. The target variable is the variety of iris. Dataset download website: You can directly use the interface provided by Scikit learn to load the iris dataset. The code is as follows: python from sklearn.datasets import load_iris iris = load_iris() X = iris.data y = iris.target Sample data: X represents the four characteristics of iris, and y represents the variety (category) of iris. Complete Python code implementation: python from sklearn.datasets import load_iris from sklearn.ensemble import BaggingClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score #Load Dataset iris = load_iris() X = iris.data y = iris.target #Divide training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #Creating and Training Bagging Models model = BaggingClassifier(n_estimators=10, random_state=42) model.fit(X_train, y_train) #Making predictions on the test set y_pred = model.predict(X_test) #Calculation accuracy accuracy = accuracy_score(y_test, y_pred) Print ("Accuracy:", accuracy) Practical summary: This practice introduces how to use the Bagging Ensemble learning method in Scikit-learn to solve the classification problem. Firstly, you need to build a Python environment and install the Scikit learn library. Then use the Iris dataset for practical operations, including loading and partitioning the dataset, constructing and training the model, making predictions on the test set, and calculating accuracy. In the actual combat process, we used the BaggingClassifier class to construct the Bagging model, using the parameter n_ Estimators specifies the number of base learners for the model. Finally, we calculated the accuracy of the model on the test set and evaluated its performance.