Configure KNeighborsClassifier "n_jobs" Parameter

The n_jobs parameter in scikit-learn’s KNeighborsClassifier controls the number of CPU cores used for the computation.

KNeighborsClassifier is a simple, instance-based learning algorithm used for classification tasks. It classifies a data point based on the majority class among its k-nearest neighbors in the feature space.

The n_jobs parameter specifies the number of CPU cores to use for the computation. It accepts integer values, where -1 uses all available cores.

The default value for n_jobs is None, which means using a single core.

In practice, common values for n_jobs include 1, 2, and -1 (using all cores), depending on the computational resources and the size of the dataset.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import time

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10,
                           n_redundant=5, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train with different n_jobs values
n_jobs_values = [1, 2, -1]
results = []

for n in n_jobs_values:
    knn = KNeighborsClassifier(n_jobs=n)
    start_time = time.time()
    knn.fit(X_train, y_train)
    end_time = time.time()
    y_pred = knn.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    elapsed_time = end_time - start_time
    results.append((n, accuracy, elapsed_time))
    print(f"n_jobs={n}, Accuracy: {accuracy:.3f}, Training Time: {elapsed_time:.2f} seconds")

Running the example gives an output like:

n_jobs=1, Accuracy: 0.905, Training Time: 0.00 seconds
n_jobs=2, Accuracy: 0.905, Training Time: 0.00 seconds
n_jobs=-1, Accuracy: 0.905, Training Time: 0.00 seconds

The key steps in this example are:

Generate a synthetic binary classification dataset with informative and redundant features.
Split the data into train and test sets.
Train KNeighborsClassifier models with different n_jobs values.
Evaluate the accuracy and training time of each model on the test set.

Some tips and heuristics for setting n_jobs:

Set n_jobs to -1 to utilize all available CPU cores for faster computation.
For smaller datasets or when computational resources are limited, use n_jobs=1.
Monitor the system’s CPU usage to ensure optimal resource utilization.

Issues to consider:

The benefit of parallel processing depends on the size and complexity of the dataset.
Using all available cores (n_jobs=-1) can lead to resource contention in multi-user environments.
Some environments (e.g., certain cloud services) may limit the number of cores available for parallel processing.

See Also