Supervised Learning

2.

The Supervised Learning Workflow

2.1.

Problem Formulation

2.1.1.

Defining the Objective

2.1.1.1.

Business Problem Translation

2.1.1.2.

Success Metrics Definition

2.1.2.

Identifying Input and Output Variables

2.1.2.1.

Feature Identification

2.1.2.2.

Target Variable Selection

2.1.3.

Understanding Business or Research Context

2.1.3.1.

Domain Knowledge Integration

2.1.3.2.

Stakeholder Requirements

2.1.3.3.

Constraints and Limitations

2.2.

Data Collection and Preparation

2.2.1.

2.2.1.1.

Internal Data Sources

2.2.1.2.

External Data Sources

2.2.1.3.

Public Datasets

2.2.1.4.

Synthetic Data Generation

2.2.2.

Data Acquisition Methods

2.2.2.1.

Database Queries

2.2.2.2.

API Integration

2.2.2.3.

2.2.2.4.

Sensor Data Collection

2.2.3.

Data Quality Assessment

2.2.3.1.

Completeness Analysis

2.2.3.2.

Consistency Checks

2.2.3.3.

Accuracy Validation

2.2.4.

2.2.4.1.

Handling Outliers

2.2.4.1.1.

Detection Methods

2.2.4.1.2.

Treatment Strategies

2.2.4.2.

Removing Duplicates

2.2.4.3.

Correcting Inconsistencies

2.2.4.4.

Standardizing Formats

2.2.5.

Data Annotation and Labeling

2.2.5.1.

Annotation Guidelines

2.2.5.2.

Quality Control Processes

2.2.5.3.

Inter-annotator Agreement

2.3.

Exploratory Data Analysis

2.3.1.

Descriptive Statistics

2.3.2.

Data Visualization

2.3.3.

Correlation Analysis

2.3.4.

Distribution Analysis

2.3.5.

Missing Data Patterns

2.4.

Feature Engineering and Selection

2.4.1.

Feature Extraction

2.4.1.1.

Domain-specific Features

2.4.1.2.

Automated Feature Extraction

2.4.2.

Feature Creation

2.4.2.1.

Polynomial Features

2.4.2.2.

Interaction Features

2.4.2.3.

Aggregation Features

2.4.3.

Feature Transformation

2.4.3.1.

Scaling and Normalization

2.4.3.2.

Encoding Categorical Variables

2.4.3.3.

Dimensionality Reduction

2.4.4.

Feature Selection Techniques

2.4.4.1.

2.4.4.1.1.

Correlation-based Selection

2.4.4.1.2.

Chi-square Test

2.4.4.1.3.

Mutual Information

2.4.4.2.

Wrapper Methods

2.4.4.2.1.

Forward Selection

2.4.4.2.2.

Backward Elimination

2.4.4.2.3.

Recursive Feature Elimination

2.4.4.3.

Embedded Methods

2.4.4.3.1.

L1 Regularization

2.4.4.3.2.

Tree-based Feature Importance

2.5.

Model Selection

2.5.1.

Criteria for Model Choice

2.5.1.1.

Problem Type Considerations

2.5.1.2.

Data Size and Dimensionality

2.5.1.3.

Interpretability Requirements

2.5.1.4.

Computational Constraints

2.5.2.

Comparing Model Families

2.5.2.1.

2.5.2.2.

Tree-based Models

2.5.2.3.

Instance-based Models

2.5.2.4.

Neural Networks

2.5.3.

Baseline Models

2.5.3.1.

Simple Baselines

2.5.3.2.

Domain-specific Baselines

2.6.

2.6.1.

Training Process Overview

2.6.1.1.

Data Preparation for Training

2.6.1.2.

Model Initialization

2.6.1.3.

Iterative Learning Process

2.6.2.

Batch vs Online Training

2.6.2.1.

Batch Training Characteristics

2.6.2.2.

Online Training Characteristics

2.6.2.3.

Mini-batch Training

2.6.3.

Monitoring Training Progress

2.6.3.1.

Loss Function Tracking

2.6.3.2.

Convergence Criteria

2.6.3.3.

2.7.

Model Evaluation

2.7.1.

Selecting Evaluation Metrics

2.7.1.1.

Task-specific Metrics

2.7.1.2.

Business-relevant Metrics

2.7.2.

Cross-validation Strategies

2.7.3.

Statistical Significance Testing

2.7.4.

Interpreting Results

2.7.4.1.

Performance Analysis

2.7.4.2.

2.7.4.3.

2.8.

Hyperparameter Tuning

2.8.1.

Identifying Tunable Hyperparameters

2.8.2.

Search Strategies

2.8.2.1.

2.8.2.2.

2.8.2.3.

Bayesian Optimization

2.8.2.4.

Evolutionary Optimization

2.8.3.

Validation Strategies for Tuning

2.8.4.

Computational Considerations

2.9.

Model Interpretation and Explainability

2.9.1.

Feature Importance Analysis

2.9.2.

Model-agnostic Explanations

2.9.3.

Local vs Global Explanations

2.10.

Deployment and Monitoring

2.10.1.

Model Serialization and Export

2.10.1.1.

2.10.1.2.

Version Control

2.10.2.

Integration into Production Systems

2.10.2.1.

API Development

2.10.2.2.

Batch Processing Systems

2.10.2.3.

Real-time Inference

2.10.3.

Monitoring Model Performance

2.10.3.1.

Performance Metrics Tracking

2.10.3.2.

Data Drift Detection

2.10.3.3.

Model Degradation Signs

2.10.4.

Retraining and Updating Models

2.10.4.1.

Retraining Triggers

2.10.4.2.

Incremental Learning

2.10.4.3.

Model Versioning

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3. Linear Models