Python's Boosting Ensemble learning Practice with Scikit learn

Preparation work: 1. Install Python: First, ensure that Python has been installed, and it is recommended to use Python version 3. 2. Install Scikit learn: Scikit learn is a machine learning library used to build, train, and evaluate various machine learning models. You can install Scikit learn through the pip command: pip install scikit-learn 3. Dataset introduction: We selected the classic Iris dataset as the sample data. This dataset contains 150 samples, each containing 4 features and 1 objective variable, for classification problems. Dataset download link: https://archive.ics.uci.edu/ml/datasets/iris Dependent class libraries: python from sklearn.datasets import load_iris from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score The complete sample code is as follows: python from sklearn.datasets import load_iris from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score #Load Dataset data = load_iris() X = data.data y = data.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) #Building a Decision Tree Classifier as a Base Classifier base_classifier = DecisionTreeClassifier(max_depth=1) #Building AdaBoost Classifiers adaboost = AdaBoostClassifier(base_classifier, n_estimators=50, learning_rate=0.1) #Training model adaboost.fit(X_train, y_train) #Prediction y_pred = adaboost.predict(X_test) #Evaluation Model accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) By running the above code, a Ensemble learning model based on AdaBoost algorithm can be realized, using the decision tree as the base classifier. Firstly, divide the dataset into a training set and a testing set, then construct an AdaBoost classifier and train the model, and finally evaluate the accuracy of the model using the testing set. Summary: Boosting is a common method in Ensemble learning, which combines multiple weak classifiers to build a strong classifier. Scikit-learn provides a powerful Boosting Ensemble learning library, including AdaBoost, Gradient Boosting, XGBoost and other algorithms. In this example, we use the AdaBoostClassifier class from Scikit learn to construct an AdaBoost classifier, and use the decision tree as the base classifier. Preparing data, building models, training and evaluating models are the basic steps for implementing Boosting Ensemble learning using Scikit lean.