所以我試圖實現(xiàn)額外的樹分類器，以便在我的數(shù)據(jù)庫中找到參數(shù)的重要性，我寫了這個簡單的代碼，但由于某種原因，我不斷得到這個錯誤。我的代碼：import numpy as npimport pandas as pdimport matplotlib.pyplot as plt%matplotlib inlinefrom sklearn.ensemble import ExtraTreesClassifier df = pd.read_csv('C:\\Users\\ali97\\Desktop\\Project\\Database\\5-FINAL2\\Final After Simple Filtering.csv')extra_tree_forest = ExtraTreesClassifier(n_estimators = 5, criterion ='entropy', max_features = 2) extra_tree_forest.fit(df)feature_importance = extra_tree_forest.feature_importances_ feature_importance_normalized = np.std([tree.feature_importances_ for tree in extra_tree_forest.estimators_], axis = 1)plt.bar(X.columns, feature_importance_normalized) plt.xlabel('Lbale') plt.ylabel('Feature Importance') plt.title('Parameters Importance') plt.show() 錯誤：TypeError Traceback (most recent call last)<ipython-input-7-4aad8882ce6d> in <module> 16 extra_tree_forest = ExtraTreesClassifier(n_estimators = 5, criterion ='entropy', max_features = 2) 17 ---> 18 extra_tree_forest.fit(df) 19 20 feature_importance = extra_tree_forest.feature_importances_TypeError: fit() missing 1 required positional argument: 'y'
1 回答
Smart貓小萌
TA貢獻1911條經驗 獲得超7個贊
通常，對于擬合函數(shù)，我們需要同時具有屬性（X）和標簽（Y），并且您需要使用它來訓練此分類器。我建議您拆分標簽和屬性，并在導入時將其作為兩個單獨的列表導入。extra_tree_forest.fit(X, Y)Final After Simple Filtering.csv
添加回答
舉報
0/150
提交
取消