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I’m very excited to announce that 6 English-language books and 9 Portuguese books have been added to the collection of over 400 free, open-source R programming books.
Forensic metascientific analysis is designed to modify trust by evaluating research consistency. It is not designed to ‘find fraud’. While this may happen, it is not the sole focus of forensic metascience as a research area and practice, it is simply the loudest consequence. The following is a guide to learning many of the available techniques in forensic metascience that have a stated quantitative approach in the tradition of Knuth’s literate programming. All code is given in R.
Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels
Simon Couch
This is a book about predictive modeling with tidymodels, focused on reducing the time and memory required to train machine learning models without sacrificing predictive performance.
Delicious recipes for getting the most out of DuckDB. This will be a continuously updated collection of recipes for DuckDB. Each chapter will focus on accomplishing a single task, with varying levels of exposition (some solutions will be obvious; others, less-so).
This book emerged from the course notes I developed as instructor for a course (STAT 341) at Colorado State University. My intent is for this to serve as a resource for an introductory-level undergraduate course on regression methods. Emphasis is on the application of methods, and so mathematical concepts are intertwined with examples using the R computing language.
Bayesian analysis of capture-recapture data with hidden Markov models: Theory and case studies in R and NIMBLE
Olivier Gimenez
Covers the authors three favorite research topics – capture-recapture, hidden Markov models and Bayesian statistics – let’s enjoy this great cocktail together
Analysis of New Additions to the Collection of Free R Programming Books
In a recent announcement, it was shared that 6 English-language and 9 Portuguese-language books have been added to an existing collection of over 400 free, open-source R programming books. This massive collection includes a Spanish-language chapter as well. Developers and learners who use the R programming language will greatly benefit from this expanded resource.
Long-term implications and possible future developments
The addition of these books to the collection implies a growing pool of resources for R programming learners and professionals. It indicates the ongoing development and interest in the R programming language and its multiple applications. As such, it can be expected that the collection will continue to grow over time. More books in more languages, with progressively diversified areas of focus may be included. This points to a likely increase in global usage and competency in R programming.
Insights into the Newly Added Books
1. ggplot2 extended by Antti Rask
This book explores how to make the most out of the whole ggplot2 ecosystem. It should be beneficial for those interested in enhancing their data visualization skills using R.
2. An Introduction To Forensic Metascience by James Heathers
This book focuses on forensic metascientific analysis evaluating research consistency. All of its code is given in R, indicating its usefulness in applying such analysis using this language.
3. Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels by Simon Couch
This book offers valuable insights into predictive modeling with tidymodels, focusing on efficient machine learning practices in R.
4. Cooking with DuckDB by Bob Rudis
This book provides recipes for getting the most out of DuckDB using R.
5. Introduction to Regression Analysis in R by Kayleigh Keller
The author’s teaching notes from Colorado State University have been transformed into this book that serves as a resource on regression methods with R.
6. Bayesian analysis of capture-recapture data with hidden Markov models: Theory and case studies in R and NIMBLE by Olivier Gimenez
This book covers three research topics – capture-recapture, hidden Markov models, and Bayesian statistics. It can be a valuable source for people interested in these subject areas.
Actionable Advice
Anyone who uses the R programming language or wishes to learn it should take advantage of this rich resource collection. Since the books are open-source and free, it offers accessible learning opportunities for everyone. The broad content coverage enables potential proficiency in various R applications and techniques, such as data visualization, forensic metascience, machine learning, regression analysis, and Bayesian statistics. Continuous learning and practice are also recommended to stay abreast with new developments and expansion of the R language.
Discover the what, why, and how of serverless machine learning with the interactive course GitHub repository.
The What, Why, and How of Serverless Machine Learning
The notion of serverless machine learning is gaining ground due to its ability to speed up processes, reduce operational complexity, and cut costs. By leveraging this technology, businesses can develop sophisticated models without the burden of managing and maintaining servers and related infrastructure.
What is Serverless Machine Learning?
Serverless machine learning is a form of computing architecture where the cloud provider dynamically manages machine learning models’ execution. It eliminates the need for organizations to maintain their server infrastructure, resulting in reduced operational costs and complexities.
Why is it Important?
Serverless machine learning offers significant benefits. It enables scalability, facilitates rapid deployment, and ensures high availability of machine learning models. Through this architecture, teams can quickly develop, test, and deploy models. Furthermore, the pay-per-use pricing model prevents unnecessary expenditure, making it cost-effective.
How Does it Work?
Serverless machine learning leverages the cloud provider’s infrastructure, so you don’t need to worry about server upkeep or scaling issues. You develop your model, upload it on the serverless platform, and it handles the execution. Once the model is deployed, the cloud provider automatically manages resources to meet workload demands.
Long-Term Implications and Future Developments of Serverless Machine Learning
In the long run, serverless machine learning could potentially revolutionize the way organizations handle data analysis and AI implementation. As complexities of data structures grow, serverless ML architectures can offer scalable, cost-effective solutions. The future may even see serverless ML become a standard for developing and deploying ML models.
Potential Developments
Increased Adoption: With reducing costs and complexities, an increasing number of businesses are expected to adopt serverless machine learning.
Advanced Frameworks: As the need for serverless technologies grows, we can anticipate the development of more sophisticated serverless machine learning frameworks and tools.
Integration with IoT: The combination of Internet of Things (IoT) and serverless architecture might bring about a new wave of innovative solutions and applications.
Actionable Insights and Recommendations
To tap into the full potential of serverless machine learning, businesses should:
Invest in Learning: The technical team should be well-versed with cloud-based serverless ML. Instead of scrambling to keep up with the technology, it’s better to be at the forefront.
Choose the Right Tools: Various serverless machine learning tools offer different features. Identify your needs and select the ones that best suit them.
Incorporate a Hybrid Model: While transitioning to a serverless architecture, keep some tasks on traditional servers until the team becomes comfortable with the new system.
Stay Updated: Serverless machine learning is evolving rapidly. Keep up with the current trends and developments to ensure you don’t get left behind.
Quantum computing is a rapidly developing field that has the potential to revolutionize information processing as we know it. Traditional computers, known as classical computers, use bits to store and process information. These bits can be in one of two states, 0 or 1, representing the binary language of computers. However, quantum computers use quantum bits, or qubits, which can exist in multiple states simultaneously, thanks to the principles of quantum mechanics.
The power of quantum computing lies in its ability to perform complex calculations at an unprecedented speed. While classical computers process information sequentially, quantum computers can process information in parallel, exponentially increasing their computational power. This means that tasks that would take classical computers millions of years to complete can be solved by quantum computers in a matter of seconds or minutes.
One of the most promising applications of quantum computing is in the field of cryptography. Current encryption methods, such as RSA, rely on the difficulty of factoring large numbers to ensure the security of data. However, quantum computers have the potential to break these encryption methods by using Shor’s algorithm, which can factor large numbers efficiently. This has raised concerns about the security of sensitive information, such as financial transactions and personal data, in a future where quantum computers are widely available.
Another area where quantum computing shows great potential is in optimization problems. Many real-world problems, such as route optimization, supply chain management, and drug discovery, involve finding the best solution among a vast number of possibilities. Classical computers struggle to solve these problems efficiently, but quantum computers can explore all possible solutions simultaneously, leading to faster and more accurate results. This could have a profound impact on various industries, from logistics to healthcare.
Furthermore, quantum computing could revolutionize the field of machine learning. Machine learning algorithms rely on processing large amounts of data to make predictions and decisions. Quantum computers can process and analyze vast amounts of data more efficiently, enabling faster and more accurate machine learning models. This could lead to advancements in areas such as image recognition, natural language processing, and drug discovery.
Despite its immense potential, quantum computing is still in its early stages of development. Building a practical and scalable quantum computer is a significant technological challenge, as qubits are fragile and prone to errors. Researchers are actively working on developing error-correcting codes and improving qubit stability to overcome these challenges.
In conclusion, quantum computing has the potential to revolutionize information processing by solving complex problems at an unprecedented speed. From cryptography to optimization problems and machine learning, the applications of quantum computing are vast and promising. While there are still significant challenges to overcome, the progress made in this field is exciting, and we can expect to see quantum computers becoming a reality in the near future. As we explore the potential of quantum computing, we are on the verge of a new era of computing that will reshape the way we process and analyze information.
[This article was first published on geocompx, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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This is the first part of a blog post series on spatial machine learning with R.
The R language has a variety of packages for machine learning, and many of them can be used for machine learning tasks in a spatial context (spatial machine learning). Spatial machine learning is generally different from traditional machine learning, as variables located closer to each other are often more similar than those located further apart. Thus, we need to consider that when building machine learning models.
In this blog post, we compare three of the most popular machine learning frameworks in R: caret, tidymodels, and mlr3. We use a simple example to demonstrate how to use these frameworks for a spatial machine learning task and how their workflows differ. The goal here is to provide a general sense of how the spatial machine learning workflow looks like, and how different frameworks can be used to achieve the same goal.
A possible workflow of the spatial machine learning task.
Inputs
Our task is to predict the temperature in Spain using a set of covariates. We have two datasets for that purpose: the first one, temperature_train, contains the temperature measurements from 195 locations in Spain, and the second one, predictor_stack, contains the covariates we will use to predict the temperature. These covariates include variables such as population density (popdens), distance to the coast (coast), and elevation (elev), among others.
We use a subset of fourteen of the available covariates to predict the temperature. But before doing that, to prepare our data for modeling, we need to extract the covariate values at the locations of our training points.
library(caret) # for modeling
library(blockCV) # for spatial cross-validation
library(CAST) # for area of applicability
library(tidymodels) # metapackage for modeling
library(spatialsample) # for spatial cross-validation
library(waywiser) # for area of applicability
library(vip) # for variable importance (used in AOA)
library(mlr3verse) # metapackage for mlr3 modeling
library(mlr3spatiotempcv) # for spatial cross-validation
library(CAST) # for area of applicability
lgr::get_logger("mlr3")$set_threshold("warn")
Model specification
Each of the frameworks has its own way of setting up the modeling workflow. This may include defining the model, the resampling method, and the hyperparameter values1. In this example, we use random forest models as implemented in the ranger package with the following hyperparameters:
mtry: the number of variables randomly sampled as candidates at each split of 8
splitrule: the splitting rule of "extratrees"
min.node.size: the minimum size of terminal nodes of 5
We also use a spatial cross-validation method with 5 folds. It means that the data is divided into many spatial blocks, and each block is assigned to a fold. The model is trained on a set of blocks belonging to the training set and evaluated on the remaining blocks. Note that each framework has its own way of defining the resampling method, and thus, the implementation and the folds may differ slightly.
For caret, we define the hyperparameter grid using the expand.grid() function, and the resampling method using the trainControl() function. In this case, to use spatial cross-validation, we use the blockCV package to create the folds, and then pass them to the trainControl() function.
The basic mlr3 steps are connected to its terminology:
Task: define the task using the as_task_regr_st() function, which specifies the target variable and the data.
Learner: define the model using the lrn() function, which specifies the model type and the hyperparameters.
Resampling: define the resampling method using the rsmp() function, which specifies the type of resampling and the number of folds. Here, we use the spcv_block resampling method.
The main function of the caret package is train(), which takes the formula, the data, the model type, the tuning grid, the training control (including the resampling method), and some other arguments (e.g., the number of trees). The train() function will automatically perform the resampling and hyperparameter tuning (if applicable). The final model is stored in the finalModel object.
In tidymodels, the fit_resamples() function takes the previously defined workflow and the resampling folds. Here, we also use the control argument to save the predictions and the workflow, which can be useful for later analysis. The fit_best() function is used to fit the best model based on the resampling results.
The mlr3 workflow applies the resample() function to the task, the learner, and the resampling method. Then, to get the final model, we use the train() function on previously defined task and learner.
After the models are trained, we want to evaluate their performance. Here, we use two of the most common metrics for regression tasks: the root mean square error (RMSE) and the coefficient of determination (R2).
RMSE and R2 are calculated by default in tidymodels. The performance metrics are extracted from the resampling results using the collect_metrics() function.
tune::collect_metrics(rf_spatial)
# A tibble: 2 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 rmse standard 1.10 5 0.0903 Preprocessor1_Model1
2 rsq standard 0.858 5 0.0424 Preprocessor1_Model1
We need to specify the measures we want to calculate using the msr() function. Then, the aggregate() method is used to calculate the selected performance metrics.
Our goal is to predict the temperature in Spain using the covariates from the predictor_stack dataset. Thus, we want to obtain a map of the predicted temperature values for the entire country. The predict() function of the terra package makes model predictions on the new raster data.
The area of applicability (AoA) is a method to assess the what is the area of the input space that is similar to the training data. It is a useful tool to evaluate the model performance and to identify the areas where the model can be applied. Areas outside the AoA are considered to be outside the model’s applicability domain, and thus, the predictions in these areas should be interpreted with caution or not used at all.
The AoA method’s original implementation is in the CAST package – a package that extends the caret package. The AoA is calculated using the aoa() function, which takes the new data (the covariates) and the model as input.
The waywiser package implements the AoA method for tidymodels2. The ww_area_of_applicability() function takes the training data and variable importance as input. Then, to obtain the AoA, we use the predict() function from the terra package.3
The CAST package can calculate the AoA for mlr3 models. However, then we need to specify various arguments, such as a raster with covariates, the training data, the variables to be used, the weights of the variables, and the cross-validation folds.
In this blog post, we compared three of the most popular machine learning frameworks in R: caret, tidymodels, and mlr3. We demonstrated how to use these frameworks for a spatial machine learning task, including model specification, training, evaluation, prediction, and obtaining the area of applicability.
There is a lot of overlap in functionality between the three frameworks. Simultaneously, the frameworks differ in their design philosophy and implementation. Some, as caret, are more focused on providing a consistent and concise interface, but it offers limited flexibility. Others, like tidymodels and mlr3, are more modular and flexible, allowing for more complex workflows and customizations, which also makes them more complex to learn and use.
Many additional steps can be added to the presented workflow, such as feature engineering, variable selection, hyperparameter tuning, model interpretation, and more. In the next blog posts, we will show these three frameworks in more detail, and then also present some other packages that can be used for spatial machine learning in R.
Footnotes
Or the hyperparameter tuning grid, in a more advanced scenario.︎
It is not a wrapper for the CAST package, but a separate implementation with some differences as you may read in the function documentation – ?ww_area_of_applicability︎
Thus, this approach allow to check the AoA for each new data set, not only the training data.︎
Analysis and Follow-up to Spatial Machine Learning with R: caret, tidymodels, and mlr3
In this follow-up analysis, we discuss crucial points from a recent blog post on spatial machine learning using R, focusing on the long-term implications, potential developments, and offering strategic advice based on the insights from the original text. The blog post highlighted three popular machine learning frameworks in R—caret, tidymodels, and mlr3—and showed how these can be used in spatial data analysis.
Key Points Discussed
The author first establishes that spatial machine learning differs from more traditional machine learning because spatially closer variables tend to bear more similarity than those located farther from each other. They proceed to use an example of predicting temperature measurements in Spain using various variables or covariates, such as population density and elevation, demonstrating the different workflows for the three R frameworks (caret, tidymodels, and mlr3).
For each framework, the author provides information on how to set up the modeling workflow, the necessary steps for loading packages, and specifics on model specification. Furthermore, the blog provides in-depth procedures on deploying data for modeling, how to evaluate performance using root mean square error (RMSE) and the coefficient of determination (R²), and how to predict future values using previously trained models through the terra package’s prediction function.
All this leads to an examination of the Area of Applicability (AoA)—a method for estimating the scope within which the model’s predictions can safely be implemented—differentiating between the customized functions each framework uses to calculate AoA.
Long-term Implications and Future Developments
Understanding and implementing spatial machine learning opens a wealth of opportunities for researchers and institutions interested in forecasting spatial variables. Regardless of the framework employed—caret, tidymodels, or mlr3—, the ability to use different covariates in creating machine learning models helps paint a comprehensive future picture, courtesy of the predictive maps.
Looking ahead, the three packages compared in the blog post offer a great starting point for spatial machine learning with R. While all three provide similar functions, their applications will continue to grow as organizations and researchers delve deeper into spatial data analysis, leading to improved prediction models, more accurate temperature measurements, and efficient data control.
As spatial machine learning advances, we can expect developments in customizability and versatility of R’s machine-learning packages, enabling researchers to include more complex variables and workflows.
Actionable Advice
Organizations and individual researchers planning to implement spatial machine learning in their work should keep the following in mind:
Choose the appropriate machine learning framework: Make a strategic choice between caret, tidymodels, and mlr3 based on the objectives of the project. Caret has a consistent and concise interface but offers limited flexibility, while tidymodels and mlr3 are more modular and flexible, albeit more complex to learn and use.
Adopt effective evaluation and prediction methods: The blog post highlights RMSE, R² and terra prediction as practical methods for evaluation and prediction in spatial machine learning. These tools should be leveraged to ascertain the effectiveness of the models.
Be mindful of the Area of Applicability: Always consider the AoA when deploying spatial machine learning. It enables the identification of areas where the model can be soundly applied and the spaces where predictions might be questionable or unreliable.
Keep learning: Explore other steps and strategies beyond the ones discussed in this blog post—feature engineering, variable selection, and hyperparameter tuning, among others.
Given the detailed instructions and comparison provided in the blog post, adopting spatial machine learning for R should be a less daunting task irrespective of the package chosen—caret, tidymodels, or mlr3.
The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that…
The article explores the growing use of intricate machine learning models in the field of education and the resulting concerns regarding their interpretability. As these models become more prevalent, there is a need to develop techniques that can provide explanations for their decisions and predictions. This article delves into the importance of explainability in machine learning and highlights the efforts being made to address this issue in the educational context.
The Role of Explainable AI in Enhancing Interpretability in Education
With the growing use of complex machine learning models in the field of education, concerns about their interpretability have emerged. The ability to understand and explain the decision-making processes of these AI systems is crucial, as it impacts their trustworthiness, ethical considerations, and overall effectiveness. In response to these concerns, there has been an increasing interest in developing explainability techniques to shed light on the inner workings of AI models, allowing educators and students to have a deeper understanding of their reasoning and recommendations.
The Challenges of Interpreting Machine Learning Models
Machine learning models, such as deep neural networks, are often referred to as “black boxes” due to their complex, non-linear nature. While these models can achieve impressive accuracy and performance, understanding how they arrive at their decisions can be challenging. In education, where transparency, fairness, and accountability are essential, the lack of interpretability poses significant obstacles.
When AI models are used to make decisions about students, such as predicting their academic performance or recommending personalized learning paths, it becomes crucial to ensure that these decisions are both accurate and explainable. For educators to trust and effectively utilize AI tools, they need to be able to comprehend the rationale behind these decisions. Similarly, students deserve to know why certain choices were made on their behalf and understand the factors that contributed to those recommendations.
Exploring Explainability Techniques
Several techniques have emerged to enhance the explainability of machine learning models in education:
Feature Importance Analysis: By examining the importance of different input features, educators and students can gain insight into which factors influenced the AI model’s decisions the most. This provides a clearer understanding and helps build trust in the system.
Rule Extraction: This technique aims to extract human-readable rules from complex AI models. By translating the learned patterns and decision rules into understandable formats, educators and students can grasp the underlying logic and reasoning employed by the model.
Interactive Visualizations: Utilizing interactive visualizations, educators and students can explore the inner workings of AI models in an intuitive manner. These visualizations can display the decision-making process, highlight influential features, and allow users to interactively investigate model behavior.
By employing these techniques, educators and students gain the ability to go beyond blindly relying on AI recommendations. Instead, they become active participants in the decision-making process, learning from AI insights and making informed choices based on a deeper understanding of the underlying data patterns.
The Promise of Explainable AI in Education
Explainable AI not only addresses interpretability concerns but also opens up new avenues for collaboration and educational exploration. By making AI models more transparent and understandable, educators and students can work alongside these systems, contributing their expertise and insights to improve them.
Furthermore, explainable AI can be a valuable learning tool in itself. By providing explanations for model decisions, students can gain deeper insights into the subject matter, better understand their own learning preferences, and receive targeted recommendations for improvement. This synergy between AI and human intelligence has the potential to revolutionize education, fostering personalized and adaptive learning experiences.
Explainable AI not only addresses interpretability concerns but also opens up new avenues for collaboration and educational exploration.
As the field of education embraces AI and machine learning, it is crucial to prioritize the development and integration of explainability techniques. By doing so, we can ensure that AI models are not only accurate but also transparent, understandable, and accountable. The combination of AI’s computational power and human expertise has the potential to create a symbiotic relationship that enhances educational outcomes and prepares students for the challenges of the future.
address this issue. Complex machine learning models, such as deep neural networks, have shown great potential in improving various aspects of education, including personalized learning, student performance prediction, and automated grading systems. However, their black-box nature has raised concerns regarding their interpretability and transparency.
The lack of interpretability in these models is a significant challenge as it hinders the understanding of how they arrive at their decisions or predictions. This is particularly crucial in educational settings, where stakeholders, including teachers, students, and parents, need to comprehend the reasoning behind the model’s outputs to ensure trust and fairness.
To tackle this issue, researchers and educators are actively exploring various explainability techniques. These techniques aim to shed light on the inner workings of complex machine learning models and provide insights into the factors influencing their predictions. By doing so, they enhance transparency, accountability, and trust in the educational applications of these models.
One approach to improving interpretability is the use of attention mechanisms. Attention mechanisms allow models to focus on specific parts of input data that are deemed important for making predictions. By visualizing these attention weights, educators can understand which features or patterns the model is prioritizing, thus gaining insights into its decision-making process.
Another promising technique is the use of rule extraction methods. These methods aim to distill complex machine learning models into simpler rule-based models that are more interpretable. By extracting understandable rules from the black-box models, educators can gain insights into the decision rules employed by these models, facilitating better understanding and trust.
Additionally, researchers are exploring methods to provide explanations alongside model predictions. These explanations can take the form of natural language explanations or visualizations that highlight the key factors considered by the model. By presenting these explanations to stakeholders, educators can ensure transparency and enable informed decision-making based on the model’s outputs.
Looking ahead, the development of explainability techniques will continue to play a crucial role in the adoption and acceptance of complex machine learning models in education. As these techniques evolve, it is expected that educators will have access to more user-friendly tools that provide clear and actionable insights into how these models work. This will not only enhance their trust in the models but also enable them to leverage the models’ capabilities more effectively to support student learning and educational decision-making.
However, it is important to acknowledge that achieving full interpretability in complex machine learning models is a challenging task. As models become more sophisticated and complex, the trade-off between interpretability and performance becomes more pronounced. Striking the right balance between accuracy and interpretability will require ongoing research and collaboration between machine learning experts and education practitioners.
In conclusion, while the increasing use of complex machine learning models in education has raised concerns about their interpretability, the development of explainability techniques offers promising solutions. These techniques, such as attention mechanisms, rule extraction methods, and explanation generation, provide insights into the decision-making processes of these models. As these techniques continue to evolve, they will play a crucial role in enhancing transparency, trust, and informed decision-making in educational settings. Read the original article
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