Glossary

11.1 Experimental Design for ML

5 min read•july 30, 2024

Experimental design is crucial for machine learning engineers to accurately assess model performance and make informed decisions. It involves controlled experiments, , and factorial designs to systematically evaluate variables and their impact on ML models.

Real-world constraints like and shape experimental design in ML. Addressing biases, determining appropriate sample sizes, and using techniques are key to creating robust experiments that yield reliable insights for improving machine learning systems.

Controlled Experiments for ML Models

Experimental Design Fundamentals

Top images from around the web for Experimental Design Fundamentals

1 of 3

Top images from around the web for Experimental Design Fundamentals

1 of 3
  • Controlled experiments in machine learning systematically manipulate variables to assess their impact on model performance while holding other factors constant
  • A/B testing compares two versions of a model or system to determine which performs better on a specific metric (click-through rates, conversion rates)
  • Factorial designs examine multiple factors and their interactions simultaneously providing a comprehensive understanding of model behavior (feature importance, hyperparameter tuning)
  • techniques estimate model performance and generalizability in experimental settings
    • divides data into k subsets, training on k-1 folds and testing on the remaining fold
    • performs multiple rounds of k-fold cross-validation to obtain more robust estimates
  • Clearly defined metrics for success objectively evaluate model performance
    • measures overall correctness of predictions
    • balances precision and recall for imbalanced datasets
    • Business-specific KPIs (, )

Real-World Considerations

  • Deployment constraints impact experimental design in ML
    • Computational resources limit model complexity and training time
    • influence model architecture and inference speed
    • Data privacy concerns restrict data usage and sharing ()
  • require specialized designs to account for temporal dependencies
    • evaluates model performance on historical data
    • simulates real-world forecasting scenarios
    • assesses model stability over time

Bias Mitigation in Experiments

Common Biases and Confounding Factors

  • occurs when the sample is not representative of the population, leading to skewed results and limited generalizability (oversampling high-income individuals)
  • correlate with both independent and dependent variables, potentially leading to incorrect conclusions about causal relationships (age influencing both income and credit score)
  • reveals trends in subgroups that disappear or reverse when groups are combined, highlighting the importance of considering all relevant variables (college admissions rates by gender and department)
  • in ML experiments occurs when the dataset only includes successful cases, leading to overly optimistic model performance estimates (analyzing only companies that survived an economic downturn)
  • inadvertently influences the training process with information from the test set, resulting in overly optimistic performance estimates (using future data to predict past events)

Mitigation Strategies

  • Careful data collection and preprocessing reduce biases
    • ensures representation of subgroups
    • techniques balance class distributions
    • and mitigate the impact of outliers
  • address confounding factors
    • pairs similar observations across treatment groups
    • isolate causal effects in the presence of confounders
    • compares changes over time between treated and control groups
  • reduce overfitting and mitigate spurious correlations
    • (Lasso) encourages sparsity in feature selection
    • (Ridge) prevents large coefficient values
    • combines L1 and L2 regularization for balanced feature selection

Sample Size and Power for ML

Statistical Power and Effect Size

  • represents the probability of correctly rejecting the null hypothesis when it is false, influenced by sample size, effect size, and significance level
  • (MDE) determines the smallest effect size reliably detected given the experimental setup
    • Smaller MDEs require larger sample sizes to maintain statistical power
    • MDEs vary based on the specific metric and business context (1% improvement in click-through rate)
  • determine required sample size for desired statistical power
    • calculates sample size before conducting the experiment
    • assesses the achieved power after the experiment
    • explores the impact of different effect sizes on required sample size

ML-Specific Considerations

  • necessitates larger sample sizes to maintain statistical power in high-dimensional feature spaces
    • Rule of thumb: 10 samples per feature for linear models, more for complex models
    • (, ) can help mitigate this issue
  • and resampling techniques estimate confidence intervals and assess model stability
    • Bootstrap sampling creates multiple datasets by sampling with replacement
    • assesses the impact of individual observations on model performance
  • determine the relationship between sample size and model performance
    • Plotting training and validation errors against sample size reveals underfitting or overfitting
    • Helps inform decisions about data collection and experimental design
  • optimizes sample sizes and experimental parameters
    • quantifies the value of additional data points
    • balances exploration and exploitation in adaptive experiments

Randomization, Blocking, and Stratification

Randomization and Blocking

  • Randomization controls for unknown confounding factors and ensures validity of statistical inferences
    • assigns treatments completely at random
    • ensures balance within blocks of a specified size
  • controls for known sources of variation, grouping experimental units into homogeneous blocks
    • Reduces within-group variability and increases statistical power
    • Example: blocking by geographic region in a multi-site ML experiment
  • efficiently allocate treatments across different conditions
    • Useful when controlling for multiple factors (model architecture, dataset, hardware)
    • Reduces the number of required experimental runs while maintaining balance

Advanced Experimental Designs

  • in experimental design ensures independent estimation of factor effects
    • Reduces multicollinearity and improves interpretability of results
    • optimize the allocation of factor levels across experimental runs
  • efficiently explore multiple factors when full factorial designs are impractical
    • Reduce the number of experimental runs while still capturing main effects and some interactions
    • estimate main effects, estimate main effects and some two-factor interactions
  • dynamically allocate resources to promising treatments
    • balance exploration of new options with exploitation of known good options
    • Thompson sampling uses Bayesian updating to guide treatment allocation based on observed outcomes
    • Useful for online ML experiments with continuous model updates and large parameter spaces

Key Terms to Review (63)

A priori power analysis: A priori power analysis is a statistical method used to determine the necessary sample size for a study, aiming to achieve a desired level of statistical power before data collection begins. This type of analysis helps researchers estimate how large their sample should be to reliably detect an effect if one exists, thereby preventing underpowered studies that may yield inconclusive results. It plays a vital role in experimental design by guiding decisions on sample sizes based on expected effect sizes, significance levels, and the desired power of the test.
A/B Testing: A/B testing is a method of comparing two versions of a webpage, app, or other product to determine which one performs better. It helps in making data-driven decisions by randomly assigning users to different groups to evaluate the effectiveness of changes and optimize user experience.
Accuracy: Accuracy is a performance metric used to evaluate the effectiveness of a machine learning model by measuring the proportion of correct predictions out of the total predictions made. It connects deeply with various stages of the machine learning workflow, influencing decisions from data collection to model evaluation and deployment.
Adaptive experimental designs: Adaptive experimental designs are flexible methodologies used in experiments that allow for modifications to the design based on interim results. This approach helps researchers make informed decisions about the direction of an experiment, such as altering sample sizes or treatment allocations, leading to more efficient and potentially more informative outcomes. In machine learning, this is particularly valuable as it can optimize the process of model selection and hyperparameter tuning, making experiments more resource-efficient.
Backtesting: Backtesting is a method used to evaluate the performance of a predictive model by applying it to historical data and measuring how well it would have performed. This process helps to determine the effectiveness of a model or strategy before deploying it in real-world scenarios, allowing practitioners to assess risk and refine their approaches. By comparing predicted outcomes with actual results, backtesting provides insights into a model's reliability and its potential for success.
Bayesian Experimental Design: Bayesian experimental design is a statistical approach that utilizes Bayesian principles to optimize the design of experiments, allowing for adaptive learning and decision-making throughout the experimental process. It combines prior knowledge with data collected during the experiment to update beliefs and improve the efficiency of learning, making it particularly useful in contexts where uncertainty is prevalent.
Blocking: Blocking is a technique used in experimental design to account for variability among experimental units by grouping similar units together. This method helps to reduce the impact of confounding variables, allowing researchers to isolate the effects of treatments more effectively. By organizing subjects into blocks, researchers can ensure that comparisons between different treatment groups are more valid and reliable.
Bootstrapping: Bootstrapping is a statistical method that involves using a small sample of data to generate many simulated samples, allowing for estimation of the distribution of a statistic. This technique is particularly useful when the sample size is limited or when the underlying distribution of the data is unknown, making it applicable in various contexts such as model training, evaluation, and bias detection.
Causal inference techniques: Causal inference techniques are statistical methods used to determine whether a relationship between two variables is causal rather than merely correlational. These techniques help in identifying the effect of an intervention or treatment on an outcome, which is crucial in making informed decisions based on data. By controlling for confounding variables and employing proper experimental or observational study designs, these methods enhance the validity of findings and aid in understanding complex systems.
Churn Rate: Churn rate is a metric that quantifies the percentage of customers or subscribers who discontinue their relationship with a service over a specific period. This term is crucial for businesses, as it provides insight into customer retention and satisfaction, indicating whether a company is effectively meeting the needs of its users. A high churn rate may signal underlying issues such as poor customer service, product dissatisfaction, or increased competition.
Computational resources: Computational resources refer to the various hardware and software assets that are essential for performing computations and executing algorithms, especially in the field of machine learning. These resources include processing power, memory, storage capacity, and network bandwidth, all of which play a critical role in the efficiency and effectiveness of machine learning experiments and models. Understanding how to allocate and optimize these resources is key to designing experiments that yield reliable and accurate results.
Confounding Factors: Confounding factors are variables that are not the primary focus of a study but can influence both the dependent and independent variables, leading to misleading conclusions. These factors can obscure the true relationship between variables, making it difficult to establish cause-and-effect links. Recognizing and controlling for confounding factors is essential in research design to ensure the validity of findings.
Controlled Experiment: A controlled experiment is a scientific study where an experimenter manipulates one variable while keeping all other variables constant to determine the effect of the manipulated variable on an outcome. This type of experiment is crucial in establishing causality and ensuring that the results are reliable and not influenced by external factors. By comparing a treatment group, which receives the intervention, to a control group, which does not, researchers can make valid conclusions about the relationship between variables.
Cross-validation: Cross-validation is a statistical method used to estimate the skill of machine learning models by partitioning data into subsets, training the model on some of these subsets, and validating it on the remaining ones. This technique helps in assessing how the results of a statistical analysis will generalize to an independent dataset, making it crucial for model selection and evaluation.
Curse of dimensionality: The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces. As the number of dimensions increases, the volume of the space grows exponentially, making it harder to sample data effectively and leading to challenges in model performance and data analysis. This phenomenon directly impacts techniques like dimensionality reduction, feature selection, and experimental design by complicating the relationships between variables and increasing the risk of overfitting.
Customer lifetime value: Customer lifetime value (CLV) is a metric that estimates the total revenue a business can expect from a single customer account throughout the business relationship. It helps businesses understand the long-term value of their customers, allowing for better allocation of resources in marketing and customer retention strategies. By calculating CLV, companies can identify which customer segments are most profitable and tailor their approaches to enhance customer relationships and loyalty.
Data augmentation: Data augmentation is a set of techniques used to artificially increase the size and diversity of a dataset by creating modified versions of existing data points. This process helps improve the performance and robustness of machine learning models by providing them with more varied training examples, thus reducing overfitting and enhancing generalization.
Data leakage: Data leakage refers to the unintended exposure of data that can lead to misleading model performance during the development and evaluation phases of machine learning. It typically occurs when the training and testing datasets overlap, allowing the model to learn from information it should not have access to, resulting in overly optimistic performance metrics and a lack of generalization to unseen data.
Data Privacy: Data privacy refers to the practice of handling and protecting personal information in a way that respects individual rights and preferences. It involves ensuring that data is collected, stored, processed, and shared responsibly, and that individuals have control over their own information. This concept is crucial across various fields, including data collection and preprocessing, the deployment of machine learning models on edge devices, the accountability of AI systems, applications in sensitive sectors like finance and healthcare, and the design of experiments that use data ethically.
Difference-in-differences analysis: Difference-in-differences analysis is a statistical technique used to estimate the causal effect of a treatment or intervention by comparing the changes in outcomes over time between a group that is subjected to the treatment and a control group that is not. This method helps control for confounding variables by looking at the differences in trends before and after the intervention, allowing researchers to infer causality more reliably.
Dimensionality Reduction Techniques: Dimensionality reduction techniques are methods used to reduce the number of input variables in a dataset while preserving its essential features. These techniques help to simplify models, enhance visualization, and improve performance by eliminating noise and redundancy from the data. By transforming high-dimensional data into lower dimensions, these methods facilitate anomaly detection and optimize experimental design in machine learning workflows.
Elastic Net: Elastic Net is a regularization technique used in linear regression that combines both L1 (Lasso) and L2 (Ridge) penalties. This approach helps to prevent overfitting by adding a penalty to the loss function that is a linear combination of the absolute values of the coefficients and the squared values of the coefficients. Elastic Net is particularly useful in scenarios where there are multiple features correlated with each other, enabling better variable selection and improved model performance.
Expected information gain: Expected information gain measures the reduction in uncertainty or entropy that a feature provides when making predictions in machine learning. It helps in evaluating how much information a particular attribute brings to the model, thus guiding feature selection and experimental design to optimize performance.
F1 score: The f1 score is a performance metric used to evaluate the effectiveness of a classification model, particularly in scenarios with imbalanced classes. It is the harmonic mean of precision and recall, providing a single score that balances both false positives and false negatives. This metric is crucial when the costs of false positives and false negatives differ significantly, ensuring a more comprehensive evaluation of model performance across various applications.
Factorial design: Factorial design is a type of experimental setup that evaluates multiple factors simultaneously to understand their effects on a response variable. This method allows researchers to study the interaction between factors, leading to a more comprehensive understanding of how different variables affect outcomes. It is particularly useful in machine learning, as it enables the efficient exploration of parameter settings and model performance.
Feature scaling: Feature scaling is the process of normalizing or standardizing the range of independent variables or features in a dataset. It ensures that each feature contributes equally to the distance calculations in algorithms, which is especially important in methods that rely on the magnitude of data, such as regression and clustering techniques.
Federated Learning: Federated learning is a machine learning approach that allows models to be trained across multiple decentralized devices while keeping the data localized on those devices. This method enhances privacy by ensuring that sensitive data never leaves its source, making it particularly relevant in scenarios where data security is paramount, like healthcare and finance. It also aligns with the principles of distributed computing by leveraging the computational power of various devices rather than relying on a centralized server.
Forward-chaining cross-validation: Forward-chaining cross-validation is a method used to evaluate machine learning models by splitting time-series data into training and testing sets in a sequential manner. This technique allows models to be trained on past data and tested on future data, preserving the temporal order of the data points, which is crucial for time-dependent predictions.
Fractional Factorial Designs: Fractional factorial designs are experimental setups that allow researchers to study multiple factors simultaneously while using a fraction of the total experimental runs required by a full factorial design. This approach is particularly useful when dealing with a large number of factors, as it saves time and resources while still providing significant insights into how these factors interact. By strategically selecting a subset of all possible combinations of factor levels, fractional factorial designs can help identify the most important variables and their relationships without exhaustive experimentation.
Instrumental Variables: Instrumental variables are tools used in statistical analysis to estimate causal relationships when controlled experiments are not feasible or when there is an issue of endogeneity. They serve as a means to isolate the variation in the independent variable that is not correlated with the error term, allowing for more accurate estimation of treatment effects. This method is crucial for ensuring that the results reflect true causal links rather than spurious correlations caused by omitted variables or measurement error.
Jackknife resampling: Jackknife resampling is a statistical technique used to estimate the precision of a sample statistic by systematically leaving out one observation at a time from the dataset. This method provides insights into the stability and reliability of the estimates produced by machine learning models, allowing for better evaluation of their performance. It's particularly useful for assessing bias and variance in predictions, which are essential components in understanding model generalization.
K-fold cross-validation: k-fold cross-validation is a statistical method used to evaluate the performance of machine learning models by partitioning the dataset into 'k' subsets or folds. This technique helps ensure that the model is tested on multiple data samples, allowing for a more reliable assessment of its predictive performance and generalizability.
L1 regularization: L1 regularization, also known as Lasso (Least Absolute Shrinkage and Selection Operator), is a technique used in machine learning to prevent overfitting by adding a penalty equal to the absolute value of the magnitude of coefficients to the loss function. This method not only helps to control model complexity but also has the unique property of performing feature selection, as it can shrink some coefficients to zero, effectively excluding those features from the model. This makes l1 regularization particularly useful when dealing with high-dimensional datasets, enhancing interpretability and improving model performance.
L2 regularization: l2 regularization, also known as weight decay, is a technique used in machine learning to prevent overfitting by adding a penalty to the loss function based on the magnitude of the coefficients. This method encourages the model to learn smaller coefficients, which leads to simpler models that generalize better to unseen data. It is particularly significant in linear and logistic regression where it helps maintain model performance while reducing complexity.
Latency Requirements: Latency requirements refer to the maximum allowable delay in processing data or delivering a response in a machine learning system. These requirements are critical for applications where timely decision-making is essential, such as in real-time analytics, autonomous systems, or interactive applications. Understanding latency requirements helps in designing experiments that account for the responsiveness of the model and the user experience.
Latin Square Designs: Latin square designs are experimental designs used to control for two potential sources of variability in experiments, ensuring that each treatment appears only once in each row and each column of a matrix. This structured approach is useful for reducing confounding variables and is particularly effective when dealing with a limited number of treatments across different conditions, making it relevant in various experimental settings.
Learning Curves: Learning curves are graphical representations that illustrate the relationship between the performance of a machine learning model and the amount of training data it has seen. These curves help to visualize how a model's accuracy improves as it learns from more data, highlighting the effectiveness of data augmentation techniques and the importance of experimental design. Understanding learning curves is essential for diagnosing issues like underfitting or overfitting, making them a critical aspect of optimizing machine learning models.
Minimum Detectable Effect: Minimum detectable effect (MDE) is the smallest effect size that an experiment can reliably detect with a given level of statistical power. It plays a crucial role in experimental design, particularly in determining the sample size needed to identify meaningful changes or impacts when implementing machine learning solutions. Understanding the MDE helps researchers and practitioners optimize their experiments, ensuring they can accurately assess the effectiveness of their interventions.
Multi-armed bandits: Multi-armed bandits refer to a class of problems in decision theory and machine learning where a gambler must choose between multiple options (or 'arms'), each with an unknown probability distribution of rewards. This problem captures the exploration-exploitation trade-off, where the gambler must decide whether to explore new arms for potentially higher rewards or exploit known arms that have provided good results in the past. The concept is crucial in designing experiments and optimizing strategies in various applications such as online advertising, clinical trials, and A/B testing.
Normalization: Normalization is the process of adjusting and scaling data values to a common range, typically to improve the performance of machine learning models. This technique ensures that different features contribute equally to the analysis, preventing any single feature from dominating due to its scale. It’s crucial during data collection and preprocessing, in pipelines, for recommender systems, time series forecasting, and when designing experiments.
Orthogonal Arrays: Orthogonal arrays are structured arrangements of experimental runs that facilitate efficient testing and analysis in experimental design. They allow researchers to study multiple factors simultaneously while ensuring that the effects of each factor can be assessed independently. This helps in optimizing resource allocation and minimizing the number of experiments needed to draw valid conclusions.
Orthogonality: Orthogonality refers to the concept of two vectors being perpendicular to each other, which in a more general sense, applies to the idea that two variables or factors do not influence each other. In experimental design, this is crucial because it helps in isolating the effects of individual factors on an outcome, leading to more reliable and interpretable results.
PCA: Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction, transforming a dataset into a new coordinate system where the greatest variance by any projection lies on the first coordinate, called the principal component. This technique helps in identifying patterns and simplifying data without losing significant information, which is crucial for tasks like anomaly detection, designing experiments, and conducting exploratory data analysis.
Permuted Block Randomization: Permuted block randomization is a method used in experimental design to assign participants to different treatment groups while ensuring that each group is balanced in size. This technique involves creating blocks of a predetermined size and then randomly permuting the order of treatments within each block. It helps minimize biases and enhances the validity of experimental results by controlling for potential confounding variables.
Post-hoc power analysis: Post-hoc power analysis is a statistical method used to determine the power of a test after data has been collected and analyzed. It assesses the likelihood that a study will detect an effect of a certain size, given the actual sample size and observed effect size. This type of analysis is particularly important for understanding whether non-significant results were due to a lack of power rather than an actual absence of an effect.
Power analysis techniques: Power analysis techniques are statistical methods used to determine the sample size needed for an experiment to detect an effect of a given size with a certain degree of confidence. These techniques help researchers ensure that their studies are adequately powered to avoid Type I and Type II errors, ultimately leading to more reliable conclusions in experimental designs.
Propensity score matching: Propensity score matching is a statistical technique used to reduce bias in observational studies by matching subjects with similar propensity scores, which estimate the probability of receiving a treatment based on observed covariates. This method helps to create comparable groups, allowing researchers to estimate causal effects more accurately while controlling for confounding variables. By aligning treated and untreated subjects with similar characteristics, this approach can improve the validity of causal inferences drawn from non-experimental data.
Randomization: Randomization is a process used in experiments to assign participants or subjects to different groups by chance, rather than by choice, ensuring that each participant has an equal opportunity of being placed in any group. This technique helps eliminate selection bias and allows for a more accurate assessment of the effect of interventions or treatments. Randomization is crucial for obtaining reliable and valid results, as it leads to groups that are statistically similar, making it easier to attribute any observed differences to the treatment itself.
Regularization techniques: Regularization techniques are methods used in machine learning to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. By constraining the model parameters, these techniques help in improving the generalization of the model on unseen data, making it more robust. Regularization plays a crucial role in experimental design as it influences how models are trained and validated, impacting their performance and reliability.
Repeated k-fold cross-validation: Repeated k-fold cross-validation is a resampling method used to evaluate the performance of machine learning models by dividing the dataset into 'k' subsets and then performing the training and testing process multiple times. Each of the 'k' subsets is used once as a test set while the remaining 'k-1' subsets form the training set. This technique helps to ensure that the model’s performance is more stable and less sensitive to how the data is divided, which is crucial for making reliable predictions.
Resolution III Designs: Resolution III designs are a type of experimental design used in the context of factorial experiments, allowing for the estimation of main effects and two-factor interactions without confounding. They are particularly useful in machine learning applications for understanding the interactions between different variables while maintaining a balance between complexity and interpretability. This design strikes a balance by incorporating enough runs to estimate relationships effectively while minimizing the number of experimental trials needed.
Resolution IV Designs: Resolution IV designs are a type of experimental design used in the field of statistics and machine learning, characterized by their ability to estimate interactions among three factors while still ensuring main effects can be estimated independently. These designs are useful for exploring complex relationships in data and for identifying which factors have the most significant influence on outcomes. The concept is critical in optimizing experiments and improving predictive modeling, allowing researchers to draw more nuanced conclusions from their studies.
Rolling Window Analysis: Rolling window analysis is a statistical method used to analyze time series data by taking a fixed-size window and moving it through the dataset to compute various metrics or perform modeling. This technique allows for the observation of how model performance or data characteristics change over time, providing insights into trends, seasonality, and potential shifts in behavior.
Selection bias: Selection bias refers to the systematic error that occurs when the sample from which data is collected is not representative of the population intended to be analyzed. This can lead to skewed results, affecting the validity of conclusions drawn from the data. It's essential to recognize and address selection bias in various contexts, including data collection, experimental design, and exploratory analysis, as it can significantly impact the accuracy and generalizability of machine learning models.
Sensitivity Analysis: Sensitivity analysis is a technique used to determine how the different values of an independent variable will impact a particular dependent variable under a given set of assumptions. It helps in understanding the effect of changes in model parameters on model outcomes, which is crucial when designing experiments or models to assess uncertainty and variability in predictions.
Simple random sampling: Simple random sampling is a statistical method where each member of a population has an equal chance of being selected for a sample. This technique is crucial in ensuring that the sample accurately represents the population, reducing bias and allowing for generalizations about the larger group based on the sample data.
Simpson's Paradox: Simpson's Paradox refers to a phenomenon in statistics where a trend appears in different groups of data but disappears or reverses when these groups are combined. This paradox highlights the importance of considering how data is grouped in experimental design, as misleading conclusions can arise if the underlying factors are not taken into account.
Statistical Power: Statistical power is the probability that a statistical test will correctly reject a false null hypothesis, essentially determining the test's ability to detect an effect when one exists. A higher power means a greater likelihood of identifying true effects in the data, which is crucial when designing experiments and interpreting results. This concept is particularly important in analyzing A/B tests and experimental designs, where it helps inform decisions regarding sample size and significance levels.
Stratified Sampling: Stratified sampling is a statistical method used to ensure that different subgroups within a population are adequately represented in a sample. This technique divides the population into distinct layers or strata based on specific characteristics, then samples from each stratum proportionally. By doing this, it enhances the representativeness of the sample, reducing bias and improving the reliability of findings in tasks like model training, evaluation, and experimental design.
Survivorship bias: Survivorship bias is a logical error that occurs when focusing on people or things that passed some selection process and overlooking those that did not. This can lead to an overly optimistic view of a situation or dataset because the failures are not accounted for. Understanding this bias is crucial in experimental design and data analysis, as it can skew results and misguide conclusions.
T-SNE: t-SNE, or t-distributed Stochastic Neighbor Embedding, is a machine learning technique used for dimensionality reduction and visualization of high-dimensional data. It helps in capturing local structures and patterns by converting similarities between data points into probabilities, making it particularly useful in exploratory data analysis and interpreting complex datasets.
Thompson Sampling: Thompson Sampling is a statistical method used for making decisions in uncertain environments, primarily focusing on maximizing rewards through exploration and exploitation strategies. This approach is particularly useful in scenarios where an agent must choose among multiple options (or arms) with unknown success rates, and it balances the trade-off between trying new options and leveraging known successful ones. The technique has strong connections to reinforcement learning and experimental design, as it provides a systematic way to learn from data while making informed decisions.
Time series experiments: Time series experiments involve collecting data points at successive time intervals to analyze trends, patterns, and dependencies over time. This method is crucial in machine learning as it helps understand temporal dynamics in data, allowing for better predictions and decision-making based on historical trends.
© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.
Back
Practice QuizGlossary
Practice QuizGlossary
Next guide