Data Science Algorithms

1. Edge Case Handling and Robustness 1.1 Edge Case Detection Identification Methods Statistical Approaches Outlier detection Anomaly detection Distribution analysis Boundary cases Domain-Specific Methods Expert rules Business logic Constraint validation Historical patterns Data-Driven Detection Clustering analysis Density estimation Distance metrics Pattern recognition 1.2 Robustness Techniques Model Hardening Data Augmentation Synthetic data generation Noise injection Perturbation […]

1. Advanced Performance Analysis 1.1 Statistical Analysis Methods Hypothesis Testing Statistical methods to evaluate model performance claims and comparisons. Techniques: Statistical Tests McNemar’s test Wilcoxon signed-rank Student’s t-test ANOVA Confidence Intervals Bootstrap estimates Cross-validation intervals Prediction intervals Error bounds Effect Size Analysis Cohen’s d Odds ratio Risk ratio Area under curve differences Error Analysis Components:

1. Algorithm Selection Methods 1.1 Selection Criteria Problem Characteristics Key Considerations: Data Type Structured vs. unstructured Numerical vs. categorical Time series vs. static Text, image, or mixed Dataset Size Small data considerations Big data requirements Memory constraints Processing limitations Problem Type Classification vs. regression Supervised vs. unsupervised Online vs. batch learning Single vs. multi-label Domain

1. Model Evaluation Techniques 1.1 Cross-Validation Methods K-Fold Cross-Validation A resampling method that divides data into k subsets, using each subset as a test set while training on others. Use Cases: Model selection Hyperparameter tuning Performance estimation Bias-variance analysis Model stability assessment Strengths: Robust evaluation Reduces overfitting Better use of data Handles small datasets Reliable

1. Ensemble Methods 1.1 Bagging (Bootstrap Aggregating) A method that creates multiple versions of a predictor by training them on random subsets of the training data and aggregating their predictions. Use Cases: Reducing overfitting Improving stability Classification tasks Regression problems Noisy data handling Strengths: Reduces variance Prevents overfitting Parallel processing possible Model stability Handles noisy

1. Time Series Analysis 1.1 Classical Methods ARIMA (AutoRegressive Integrated Moving Average) A statistical model that combines autoregression, differencing, and moving average components for time series forecasting. Use Cases: Financial forecasting Sales prediction Weather forecasting Demand planning Traffic prediction Strengths: Handles trends and seasonality Well-understood statistical properties Good for linear relationships Interpretable components Works with

1. Generative Models 1.1 Generative Adversarial Networks (GANs) A framework where two neural networks compete: a generator creating synthetic data and a discriminator trying to distinguish real from fake data. Use Cases: Image synthesis Data augmentation Style transfer Text-to-image generation Video generation Strengths: High-quality synthetic data Learns complex distributions Unsupervised learning Creative applications Continuous improvement

1. Basic Neural Networks 1.1 Feedforward Neural Networks (FNN) The most basic neural network architecture where information flows in one direction, from input through hidden layers to output, without cycles. Use Cases: Pattern recognition Classification tasks Regression problems Function approximation Feature learning Strengths: Simple to understand Versatile Good for structured data Fast inference Well-studied architecture

1. Dimensionality Reduction Techniques 1.1 Principal Component Analysis (PCA) A linear dimensionality reduction technique that transforms high-dimensional data into a new coordinate system of orthogonal axes (principal components) that maximize variance. Use Cases: Image compression Feature extraction Data visualization Pattern recognition Noise reduction Strengths: Simple and interpretable Computationally efficient Preserves maximum variance Handles correlated features

1. Clustering Algorithms 1.1 K-Means An iterative algorithm that partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean. Use Cases: Customer segmentation Image compression Document clustering Anomaly detection Pattern recognition Strengths: Simple to understand and implement Scales well to large datasets Fast convergence Memory efficient Works well

1. Logistic Regression A statistical model that uses a logistic function to model a binary dependent variable. Despite its name, it’s used for classification rather than regression. Use Cases: Credit card fraud detection Email spam classification Disease diagnosis Customer churn prediction Marketing campaign response prediction Strengths: Simple and interpretable Computationally efficient Provides probability scores Works

1. Linear Regression A linear approach to modeling the relationship between a dependent variable and one or more independent variables, assuming a linear relationship. Use Cases: Price prediction Sales forecasting Risk assessment Resource allocation Performance prediction Strengths: Simple and interpretable Computationally efficient Clear feature impact through coefficients Easy to implement and maintain Good baseline model