Python으로 배우는 이상치 탐지
Bekhruz (Bex) Tuychiev
Kaggle Master, Data Science Content Creator
import pandas as pd males = pd.read_csv("ansur_male.csv") males.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4082 entries, 0 to 4081 Data columns (total 95 columns): # Column Non-Null Count Dtype 0 abdominalextensiondepthsitting 4082 non-null int64 1 acromialheight 4082 non-null int64 2 acromionradialelength 4082 non-null int64 3 anklecircumference 4082 non-null int64 4 axillaheight 4082 non-null int64 ...
from pyod.models.knn import KNN knn = KNN(contamination=0.01, n_jobs=-1) knn.fit(males)
probs = knn.predict_proba(males) # Use 55% threshold for filtering is_outlier = probs[:, 1] > 0.55 # Isolate the outliers outliers = males[is_outlier] len(outliers)
probs = knn.predict_proba(males)
# Use 55% threshold for filtering is_outlier = probs[:, 1] > 0.55 # Isolate the outliers outliers = males[is_outlier] len(outliers)
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# k=20 when contamination is <=10% knn = KNN(n_neighbors=20, n_jobs=-1) knn.fit(males) probs = knn.predict_proba(males) is_outlier = probs[:, 1] > .55 outliers = males[is_outlier] len(outliers)
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