我正在看这个教程：https ://www.dataquest.io/mission/75/improving-your-submission

在第 8 节，寻找最佳特征，它显示了以下代码。

import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif

predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked", "FamilySize", "Title", "FamilyId"]

# Perform feature selection
selector = SelectKBest(f_classif, k=5)
selector.fit(titanic[predictors], titanic["Survived"])

# Get the raw p-values for each feature, and transform from p-values into scores
scores = -np.log10(selector.pvalues_)

# Plot the scores.  See how "Pclass", "Sex", "Title", and "Fare" are the best?
plt.bar(range(len(predictors)), scores)
plt.xticks(range(len(predictors)), predictors, rotation='vertical')
plt.show()

k=5 是做什么的，因为它从未被使用过（图表仍然列出了所有功能，无论我使用 k=1 还是 k="all"）？它如何确定最佳特征，它们是否独立于人们想要使用的方法（无论是逻辑回归、随机森林还是其他）？