擅长:python、mysql、java
<p>我找到了一个解决办法,虽然看起来很老套,如果这是Python的方式。在</p>
<pre><code>iris_df = pd.read_csv('https://raw.githubusercontent.com/mpourhoma/CS4661/master/iris.csv')
feature_cols = ['sepal_length','sepal_width','petal_length','petal_width']
X = iris_df[feature_cols]
y = iris_df['species']
predictions= {}
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=6)
k = 3
my_knn_for_cs4661 = KNeighborsClassifier(n_neighbors=k)
for col in feature_cols:
my_knn_for_cs4661.fit(X_train[col].values.reshape(-1,1), y_train)
y_predict = my_knn_for_cs4661.predict(X_test[col].values.reshape(-1,1))
predictions[col] = accuracy_score(y_test, y_predict)
print(predictions)
</code></pre>