相关性阈值，它会去掉那些高度相关的特征(亦即，这些特征的特征值变化与其他特征非常相似)。它们提供的是冗余信息。

find_correlation.py

import pandas as pd
import numpy as np

def find_correlation(df, thresh=0.9):
    """
    Given a numeric pd.DataFrame, this will find highly correlated features,
    and return a list of features to remove
    params:
    - df : pd.DataFrame
    - thresh : correlation threshold, will remove one of pairs of features with
               a correlation greater than this value
    """
    
    corrMatrix = df.corr()
    corrMatrix.loc[:,:] =  np.tril(corrMatrix, k=-1)

    already_in = set()
    result = []

    for col in corrMatrix:
        perfect_corr = corrMatrix[col][corrMatrix[col] > thresh].index.tolist()
        if perfect_corr and col not in already_in:
            already_in.update(set(perfect_corr))
            perfect_corr.append(col)
            result.append(perfect_corr)


    select_nested = [f[1:] for f in result]
    select_flat = [i for j in select_nested for i in j]
    return select_flat