Johnny's Knowledge

K Nearest Neigbour - Implementation Example

Properties1
tagsmachine-learning, supervised-learning

Implementation Example

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
 
# Scale features (essential!)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
 
# Initialize KNN
knn = KNeighborsClassifier()
 
# Hyperparameter tuning
param_grid = {
    'n_neighbors': [3, 5, 7, 9, 11],
    'weights': ['uniform', 'distance'],
    'metric': ['euclidean', 'manhattan']
}
 
grid_search = GridSearchCV(knn, param_grid, cv=5)
grid_search.fit(X_train_scaled, y_train)
 
# Best model
best_knn = grid_search.best_estimator_
test_score = best_knn.score(X_test_scaled, y_test)

Backlinks