机器学习中如何处理分类变量的不均衡

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Imbalanced classes put “accuracy” out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class

  • Up-sample the minority class

    • resample module with replace = True

  • Down-sample the majority class

    • resample module with replace = False

  • Change your performance metric

    • Area Under ROC Curve (AUROC)
    • from sklearn.metrics import roc_auc_score

  • Penalize algorithms (cost-sensitive training)

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SVC(kernel='linear', 
            class_weight='balanced', # penalize
            probability=True)

  • Use tree-based algorithms

    • from sklearn.ensemble import RandomForestClassifier
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import numpy as np
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.utils import resample


input = pd.read_csv('trime_skinput.csv') # row.names equls numbers
input.trimethoprim_sulfamethoxazole.value_counts()
input_major = input[input.trimethoprim_sulfamethoxazole == "R"]
input_minor = input[input.trimethoprim_sulfamethoxazole == "S"]

input_minor_upsampled = resample(input_minor,
                                 replace = True,
                                 n_samples = 67,
                                random_state=123)

input_upsampled = pd.concat([input_major,input_minor_upsampled])
input_upsampled.trimethoprim_sulfamethoxazole.value_counts()


X = input_upsampled.iloc[:,0:19277]
y = input_upsampled.iloc[:,19277:19278]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)

### train model
from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')

svclassifier.fit(X_train, y_train)

## predict 
y_pred = svclassifier.predict(X_test)

# evaluation 
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))
# upsampling
              precision    recall  f1-score   support

           R       1.00      0.88      0.93        16
           S       0.85      1.00      0.92        11

    accuracy                           0.93        27
   macro avg       0.92      0.94      0.93        27
weighted avg       0.94      0.93      0.93        27

## don't do anything
                precision    recall  f1-score   support

           R       0.88      1.00      0.93        14
           S       0.00      0.00      0.00         2

    accuracy                           0.88        16
   macro avg       0.44      0.50      0.47        16
weighted avg       0.77      0.88      0.82        16

参考资料