Python uses Scikit learn cross validation to evaluate model performance

Before using Scikit learn for cross validation to evaluate model performance, we need to first build a Python environment and install the required class libraries. The following are the steps for preparation: 1. Environmental construction: -Installing Python: Scikit learn is a machine learning library written in Python, so you need to first install Python. You can access it from the official Python website（ https://www.python.org/ ）Download the Python version suitable for your operating system. -Install pip: pip is a package management tool for Python, used to install and manage Python class libraries. After installing Python, you can use the following command to install pip: python get-pip.py -Install Scikit learn: Use the following command to install the Scikit learn class library: pip install -U scikit-learn 2. Dependent class libraries: -Scikit learn: Scikit learn is a popular Python machine learning library that provides many tools for data analysis and modeling. -Numpy: Numpy is a scientific computing library in Python that provides multidimensional array objects and functions for handling these arrays. -Pandas: Pandas is a data analysis library that provides data structures and functions for Data cleansing, processing and analysis. -Matplotlib: Matplotlib is a drawing library for Data and information visualization. 3. Dataset: We will use the Iris dataset provided by Scikit-learn as an example dataset. This dataset contains measurement data for iris from three different species (Setosa, Versicolor, Virginia). This dataset has 150 samples, each with 4 features (calyx length, calyx width, petal length, petal width). You can use the following code to download the dataset and load it into Python: python from sklearn.datasets import load_iris iris = load_iris() X, y = iris.data, iris.target 4. Implement a complete example: python from sklearn.datasets import load_iris from sklearn.model_selection import cross_val_score from sklearn.tree import DecisionTreeClassifier #Download and load the Iris dataset iris = load_iris() X, y = iris.data, iris.target #Create Decision Tree Classifier clf = DecisionTreeClassifier() #Evaluating classifier performance using cross validation scores = cross_val_score(clf, X, y, cv=5) #Print the accuracy of each cross validation print('Accuracy:', scores) #Print average accuracy print('Average Accuracy:', scores.mean()) 5. Summary: Scikit learn is a powerful Python machine learning library that provides rich functionality and tools. Using Scikit learn for cross validation to evaluate model performance can effectively evaluate the accuracy of the model and tune it. In the above example, we downloaded the Iris dataset, created a decision tree classifier, and used cross validation for model evaluation. Finally, we printed the accuracy of each cross validation and calculated the average accuracy.