我尝试了以下代码，但出现了这个错误

数据集的链接在下方

ValueError—> 第18行 ds1_model.fit(X, y)

ValueError: 无法将字符串转换为浮点数: ‘Iris-setosa’

  import pandas as pdfrom sklearn.metrics import mean_absolute_errorfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.model_selection import train_test_spliturl = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv'ds1 = pd.read_csv(url)ds1.columns = (['SepalLength' , 'SepalWidth' , 'PetalLength' , 'PetalWidth' , 'ClassLabel'])ds1_filtered=ds1.dropna(axis=0)y = ds1_filtered.ClassLabelds1_features = ['SepalLength' , 'SepalWidth' , 'PetalLength' , 'PetalWidth']X = ds1_filtered[ds1_features]ds1_model = DecisionTreeRegressor()ds1_model.fit(X, y)PredictedClassLabel = ds1_model.predict(X)mean_absolute_error(y, PredictedClassLabel)train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)ds1_model = DecisionTreeRegressor()ds1_model.fit(train_X, train_y)predicitions = ds1_model.predict(val_X)print(mean_absolute_error(val_y, predictions))

您能帮助建议或解释如何修复这个问题吗？

数据集链接

回答：

正如ClassLabel名称所暗示的，鸢尾花数据集是一个分类数据集，而不是回归数据集；因此，既不应该使用DecisionTreeRegressor模型，也不应该使用mean_absolute_error作为评估指标。

您应该使用DecisionTreeClassifier和accuracy_score来代替：

from sklearn.datasets import load_irisfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scoreiris = load_iris()clf = DecisionTreeClassifier()train_X, val_X, train_y, val_y = train_test_split(iris.data, iris.label, random_state = 0)clf.fit(train_X, train_Y)pred = clf.predict(val_X)print(accuracy_score(val_y, pred))

scikit-learn的决策树分类教程使用上述数据集可以为您提供更多思路。