iris数据集机器学习曲线显示

好的，我可以为您提供iris数据集的机器学习曲线示例。以下是使用Python和Scikit-learn库绘制的一个简单的示例代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import learning_curve
import matplotlib.pyplot as plt

# 加载iris数据集
iris = load_iris()
X, y = iris.data, iris.target

# 定义分类器
from sklearn.svm import SVC
clf = SVC(kernel='linear', C=1, random_state=42)

# 绘制学习曲线
train_sizes, train_scores, test_scores = learning_curve(
    clf, X, y, cv=10, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5), verbose=0)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)

# 画出学习曲线
plt.figure()
plt.title("Learning Curve (SVM, linear kernel)")
plt.xlabel("Training examples")
plt.ylabel("Score")
plt.grid()

plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                 train_scores_mean + train_scores_std, alpha=0.1,
                 color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                 test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
         label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
         label="Cross-validation score")

plt.legend(loc="best")
plt.show()

这段代码会生成一个学习曲线图，其中包含了训练集和交叉验证集的得分随着训练样本数量的增加而变化的趋势。您可以根据需要调整代码中的参数，例如分类器类型、训练集大小等等。