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法学如何使用Python Scikit-learn实现线性分类？
线性分类是最简单的机器学习问题之一。为了实现线性分类，我们将使用sklearn的SGD（随机梯度下降）分类器来预测鸢尾花种类。
步骤
您可以按照以下步骤使用Python Scikit-learn实现线性分类：
步骤1 - 首先导入必要的包：scikit-learn、NumPy和matplotlib
步骤 2 - 加载数据集并从中构建训练集和测试集。
步骤 3 - 使用matplotlib绘制训练实例。虽然此步骤是可选的，但为了更清晰起见，绘制实例是一种良好的实践。
步骤 4 - 创建SGD分类器的对象，初始化其参数，并使用fit()方法训练模型。
步骤 5 - 使用Python Scikit-learn库的metrics包评估结果。
示例
让我们检查下面的示例，我们将使用其两个特征（萼片宽度和萼片长度）来预测鸢尾花种类：
# Import required libraries import sklearn import numpy as np import matplotlib.pyplot as plt # %matplotlib inline # Loading Iris flower dataset from sklearn import datasets iris = datasets.load_iris() X_data, y_data = iris.data, iris.target # Print iris data shape print ("Original Dataset Shape:",X_data.shape, y_data.shape) # Dividing dataset into training and testing dataset and standarized the features from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # Getting the Iris dataset with only the first two attributes X, y = X_data[:,:2], y_data # Split the dataset into a training and a testing set(20 percent) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1) print ("\nTesting Dataset Shape:", X_train.shape, y_train.shape) # Standarize the features scaler = StandardScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) # Plot the dataset # Set the figure size plt.figure(figsize=(7.16, 3.50)) plt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95) plt.title('Training instances', size ='18') colors = ['orange', 'green', 'cyan'] for i in range(len(colors)): px = X_train[:, 0][y_train == i] py = X_train[:, 1][y_train == i] plt.scatter(px, py, c=colors[i]) plt.legend(iris.target_names) plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.show() # create the linear model SGDclassifier from sklearn.linear_model import SGDClassifier linear_clf = SGDClassifier() # Train the classifier using fit() function linear_clf.fit(X_train, y_train) # Print the learned coeficients print ("\nThe coefficients of the linear boundary are:", linear_clf.coef_) print ("\nThe point of intersection of the line are:",linear_clf.intercept_) # Evaluate the result from sklearn import metrics y_train_pred = linear_clf.predict(X_train) print ("\nThe Accuracy of our classifier is:", metrics.accuracy_score(y_train, y_train_pred)*100)
输出
它将产生以下输出：
Original Dataset Shape: (150, 4) (150,)
Testing Dataset Shape: (120, 2) (120,)
The coefficients of the linear boundary are: [[-28.85486061 13.42772422]
[ 2.54806641 -5.04803702]
[ 7.03088805 -0.73391906]]
The point of intersection of the line are: [-19.61738307 -3.54055412 -0.35387805]
分类器的准确率为：76.66666666666667
更新于：2022年10月4日
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