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初级

KNN-多分类报告

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初级参考 完整示例代码供参考，建议自己理解后重新输入 
def solve():
    import pandas as pd
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import classification_report
    df = pd.read_csv('https://liangdaima.com/static/data/iris.csv')
    X = df.drop('species', axis=1)
    y = df['species']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    knn = KNeighborsClassifier(n_neighbors=3)
    knn.fit(X_train, y_train)
    y_pred = knn.predict(X_test)
    print(classification_report(y_test, y_pred))

示例

输入
solve()
期望输出
precision    recall  f1-score   support

      setosa       1.00      1.00      1.00        10
  versicolor       1.00      1.00      1.00         9
   virginica       1.00      1.00      1.00        11

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30
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