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3. API Reference: KNNImputer

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Done

Complete `KNNImputer` Professional Guide

A Comprehensive Reference for Data Scientists

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Introduction & Fundamentals

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Mathematical Foundation

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Implementation Basics

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Parameter Deep Dive

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Practical Examples

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Advanced Techniques

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Performance Optimization

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Best Practices

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Troubleshooting Guide

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Real-World Case Studies

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Advanced Performance Monitoring

Production Deployment Guide

Scalable KNN Imputation Pipeline

class ProductionKNNImputer:
    """
    Production-ready KNN imputation with monitoring and error handling
    """

    def __init__(self, config_path=None):
        self.config = self._load_config(config_path)
        self.imputer = None
        self.scaler = None
        self.encoders = {}
        self.feature_names = None
        self.is_fitted = False

    def _load_config(self, config_path):
        """Load configuration from file or use defaults"""
        default_config = {
            'n_neighbors': 5,
            'weights': 'distance',
            'algorithm': 'auto',
            'chunk_size': 10000,
            'n_jobs': -1,
            'validation_split': 0.2,
            'max_memory_gb': 8
        }

        if config_path:
            import json
            with open(config_path, 'r') as f:
                user_config = json.load(f)
            default_config.update(user_config)

        return default_config

    def _estimate_memory_usage(self, n_samples, n_features):
        """Estimate memory usage for KNN imputation"""
        # Rough estimate: distance matrix + feature matrix
        distance_matrix_gb = (n_samples ** 2 * 8) / (1024 ** 3)
        feature_matrix_gb = (n_samples * n_features * 8) / (1024 ** 3)
        total_gb = distance_matrix_gb + feature_matrix_gb

        return total_gb

    def _preprocess_data(self, df

3. API Reference: KNNImputer

Complete KNNImputer Professional Guide

Table of Contents

Introduction & Fundamentals

Mathematical Foundation

Implementation Basics

Parameter Deep Dive

Practical Examples

Advanced Techniques

Performance Optimization

Best Practices

Troubleshooting Guide

Real-World Case Studies

Advanced Performance Monitoring

Production Deployment Guide

Scalable KNN Imputation Pipeline

Complete `KNNImputer` Professional Guide