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): |