View knn_impute_example.py
knn_impute(target=df['Age'], attributes=df.drop(['Age', 'PassengerId'], 1), | |
aggregation_method="median", k_neighbors=10, numeric_distance='euclidean', | |
categorical_distance='hamming', missing_neighbors_threshold=0.8) |
View knn_impute_example.py
knn_impute(target=df['Age'], attributes=df.drop(['Age', 'PassengerId'], 1), | |
aggregation_method="median", k_neighbors=10, numeric_distance='euclidean', | |
categorical_distance='hamming', missing_neighbors_threshold=0.8) |
View knn_impute.py
import numpy as np | |
import pandas as pd | |
from collections import defaultdict | |
from scipy.stats import hmean | |
from scipy.spatial.distance import cdist | |
from scipy import stats | |
import numbers | |
def weighted_hamming(data): |