The Data Blog
In data science, model training and fitting via machine learning is one of those subjects that never really has the same answer each time. Every model is different; each has their own data, response, and predictors uniquely akin to them. Yes there is a "right way" and a "wrong way" to train a fit a model, but what is right and wrong is very subjective. If the model works does that mean you trained it right every step of the way? If the model predictions are inconclusive or opposite of your hypothesis did you train it wrong?
The first step in proper model training is always asking yourself what the goal of the model is. What is its purpose? What are you trying to predict? If you can't specifically summarize your aim in a sentence or two you need to reevaluate your goals and the conceptual idealism behind the model. Every model should have a clear purpose that can be easily explained to anyone willing to listen.
"I'm testing to see if the amount of sleep one gets is a reliable predictor to whether or not they will drink coffee the next morning."
Great, right? Wrong. While the above description certainly seems valid, so many questions already arise from that one sentence. Does the person have to usually drink coffee to be counted as valid in the analysis? What do you mean by the 'amount of sleep', do you mean a little, a lot, just enough? When does the morning end? What defines "reliable" in this context?
To be honest, we can nitpick even the greatest models, but at the very least, a great model's problem, objective, solution, and features should be clearly identifiable when summarizing it.
"My model predicts whether or not the average caffeine consumer will drink coffee within four hours of waking up if they got less than 8 hours of sleep the previous night."
After being able to summarize your model and clearly laying out your intent, you'll need the right data to back it up. A few questions to keep in mind; how will you get the data? How do you know the data is accurate? Will you filter out outliers or take a random sample of the amount of observations? What if some of your data is incomplete or missing? Which fields are more important than others; which are predictors, which are responses, and which are unnecessary? Is there multicollinearity or any correlation at all for that matter within your data?
There are many questions that need to be addressed with the data, so many that they can't all be listed here. However, the most important part is that you're able to validate your data and the model that depends on it, so you can move forward with training your model.
We may train their model in a number of ways, one of the most popular being with a linear regression fit aimed to answer a specific question; what is the relationship between our predictors and outcome? While training our data we hope to isolate which of our predictors have a significant impact on our outcome and continue to use them, while taking out those which do not have an impact on our data in the long-run. By analyzing which variables have the most impact on our our response as a whole, we're able to enact the process known as regularization. Regularization is important, as it will determine your model's capabilities on data other than your training set. If your model doesn't work as well on other types of data meant for it, your model may be under or overfit, and you'll have to back up a bit in terms of figuring out your best predictors.
Thinking back to our coffee example, our response variable is obviously whether or not a consumer drank coffee. The most obvious predictor would include amount of sleep the previous night, but should include other predictors as well such as age, sex, weight, and accessibility. We'd then aim to trim any variable deemed not a valid predictor for drinking coffee the next morning. Once we believe we have the best predictors for our model, we'd test it on other datasets and continue training it until we're satisfied.
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A new year means new predictions for data trends and how they will affect the world of technology! Several trends are expected such as an increase in cloud computing, a large migration of systems to current databases to cloud software, and data privacy and social media data harvesting continuing to be in the spotlight of many.
Thus, to get the jump on others, it may be in your best interest to act quickly to migrate systems or get the next generation for your data needs. Whether it's for testing purposes, storage, or analytics, the future is tomorrow and tomorrow will come faster than you think.
We recommend researching upcoming data driven techniques that fit your need and capabilities and comparing them to your current processes right now. Do the upcoming or freshly introduced technologies look better than what you currently have? If so, you may have to act quickly before competitors jump on board and are the first to invest. So where can you start looking for these up and coming data driven technologies? Well, you've come to the right place.
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There are many ways synthetic data can be used to help grow, strengthen, and rejuvenate your organization and many processes it handles, but here are five key ways in which synthetic data will be able to directly help you and your company!
1) Synthetic Data has a wide variety of use cases to help you out with. Synthetic Data is artificially generated and thus can be manipulated for production testing and model fitting in a plethora of ways. It can be used for machine learning, mathematical model fitting, model testing, and more!
2) Synthetic Data adds an extra layer of security to your data; because synthetic data is artificially generated, if there is a data leak, hack, or if something ends up going wrong, there will be minimal security risk and harm as the exposed data will not put any individual's private information in danger of being exploited. This factor is huge within the cybersecurity world and adds as an extra precaution just in case there is a breach in the system.
3) Synthetic Data is cost-effective. Synthetic Data is less expensive to generate than it is to buy real data in terms of both time and money. Furthermore, because you may need different types of data for different types of test, you'll need several different types of data to test with; this begs the question, wouldn't it be easier to generate each type on the fly as needed rather than stat testing, realize you need to collect more samples and pause testing until you have collected enough to continue?
4) Synthetic Data is great when it comes to threat detection. Synthetic data can reflect authentic patterns and behaviors for insider threat detection and user behavior in the models it is used to create. Furthermore, it can be used during performance testing to cover a variety of different scenarios which can lead to increased threat detection and strengthen an application or model's defensive capabilities.
5) Synthetic Data strengthens performance more than authentic data can. Synthetic data can be used to test models with quickly and efficiently so that data can be analyzed right after the data is plugged in. Moreover, it can be used to train models in ways models can't be trained when using authentic data; it can be generated to fill in for any missing data or used to predict different types of behavior based on reasonable machine learning, rather than leaving data empty or assuming what 'would' have been answered.
The COVID-19 is one of the most dangerous problems we as a society struggle with today, and to make matters worse the disease is highly contagious and spreading rapidly around the world. As there are many people who are unaware of their health situation and don't find it necessary to get tested, and furthermore aren't enough test kits readily available for every single person, it's essential we use our resources and historical data to track the virus so we can begin to stop it in its tracks.
By preparing travel, social, and contact networks, we may effectively be able to track to a certain degree where the virus is, isn't, and may potentially be. A travel network specifies a single, series, or pattern of travel activities by a node [individual] or group of nodes [group of individuals] by any mode to any location. A social network is defined as a network of known social interactions between family, friends, co-workers, and those you are relatively familiar with. Meanwhile, a contact network tracks the time and proximity one node may have to another at any given time, but isn't specifically limited to others known by the individual; contact networks include interactions with a cashier when buying a coffee or perhaps passing someone nearby on local transportation. By combining the three types of networks, we effectively can understand each node's travel, social, and contact patterns and compare them to COVID-19's own pattern of travel, something we can denote as contact tracing.
Using the data collected from the COVID-19 outbreak as well as by those who have been tested for exposure, we have the opportunity to track the precise whereabouts of the pandemic and fight it before the next wave of it or a future pandemic begins. The first of our two key assumptions for this methodology is we have enough readily available data to use for tracking where COVID-19 has been and currently is so we can also predict where it is likely to go. The second key assumption is that we find a way to track those we don't have data on, as the contact network isn't limited to interactions with known nodes, but unknown ones as well. Nevertheless, this is a rare opportunity we have to begin our fight back against COVID-19 and other future pandemics, and we should take any advantage we can to prepare for it.
A thought exercise on the System perspective of dev and test, as enabled by ExactData Synthetic Data.
Let’s consider the development of an application that scours incoming data for fraudulent activity… How would that test and analysis look with production data, de-identified production data, hand crafted data, and ExD synthetic data?
Let’s also consider that the application will classify all transactions/events as either bad or good. The perfect application would classify every transaction correctly resulting in 100% Precision (everything classified as bad was actually bad), 100% capture rate (classified every actual bad as bad), 0% escape rate (no bads classified as good), and 0% False Positive rate (no goods classified as bad). The application needs to be developed, tested, and analyzed from a System perspective. For example, the application could classify every transaction as bad and achieve 100% capture rate, and 0% escape rate, but would also result in poor Precision and a huge False Positive rate – thus requiring significant labor support to adjudicate the classifications. On the other extreme, the application could classify everything good, be mostly right, and not catch any bads. Both of these boundary conditions are absurd but illustrate the point of the importance of System.
One method of System analysis is the Confusion Matrix, noted below.
With production data, you don’t know where the bads are, so you can’t complete the confusion matrix.
With de-identified production data, you don’t know where the bads are, so you can’t complete the confusion matrix.
With hand-crafted data, you might have the “truth” to enable completion of the confusion matrix, you would not have the complexity or volume to be truly testing to find the “needle” in the haystack of fraudulent behavior within mass of good behavior.
With ExD synthetic data, you know where every bad is (you have the ground truth), so you CAN complete all 4 quadrants of the confusion matrix, and can then only, conduct a system analysis, driving the application to the real goal of tuning and optimizing Precision (maximizing TP) and Capture rate (maximizing TP/TP+FN) , while at the same time minimizing Escapes (FN) and False Positive rate (FP/FP+TP). Within a particular setup of an application version, these are typically threshold trade-offs, but with next iteration development, there is the opportunity to improve on all scores.
With every new year comes exciting new updates and trends to the technological world around us! We at ExactData are excited about many trends and future advancements to come, but here are five that we're excited about in particular!
1) Advancement of AI and Mobile Intelligence
It's no secret that AI and mobile intelligence are evolving everyday. We see growth in both of these departments to no end, where things like facial recognition, fingerprint, voice, and eyes scans are all becoming more of a reliable reality! This is seen through many of the innovations of Apple, Samsung, and Google have brought to the table, but also through other fields of data science as well!
2) Automation and Innovation
When one thinks of automation and innovation, jobs and mundane tasks are often the first things thought of. How is data being innovated or automated you may ask? Well being able to derive data in faster response rates, being able to generate, switch, and use data for test purposes on the fly for exact results seems innovative to us! This innovation can be traced to artificial intelligence as well through pattern recognition, GPS sensors, self-driving cars, and more!
3) Cloud Computing and Cyber Security
Cloud computing is becoming more distributed, meaning the origin of the cloud can distribute services to other locations while operating fully in effect from one area. Server updates, latency checks, and bandwidth fixes are becoming quicker every year which not only affects the cloud and its functions but can also be used to stop breaches, glitches, and hackers right in their tracks as soon as they get into the system.
4) Financial Patterns and Recognition
Recognizing financial data patterns through data has been historically tricky due to the immense analytical prowess and and observational skills that could be needed. AI and statistical learning developments however can be trained to pick up these patterns more quickly than ever before, and with less error too. Financial analytics and trend recognition will certainly see upgrades in the upcoming year, especially with more variables such as cryptocurrency coming into play.
5) Accessibility and Privacy
Accessibility and privacy for data files come hand in hand; by making something more accessible you also have the means to make it more restricted. Added levels of security for data can come in many different forms; test data, artificial data, cloud computing, advanced machine learning, more advanced security protocols and more. The rule of thumb is to keep everything private that you may need for later so that nobody else can take or modify it.
While there are so many trends we believe to be up and coming in the world of data, these were just some of the few we believe to be relevant to both the industry and general public as a whole.
The terms "database" and "database management system" are typically used interchangeably despite the fact the two mean completely separate things. Additionally, both are important terms that those in the technology industry should clearly know how to distinct between, but it seems many people either don't or can't. Very quickly, below are definitions for the two vocabulary terms.
A database is a logically modeled cluster of information [data] that is typically stored on a computer or other type of hardware that is easily accessible in various ways.
A database management system is a computer program or other piece of software that allows one to access, interact with, and manipulate a database.
Additionally, there are many types of database management systems that exist in the world today. Historically, relational database management systems (RDBMS) are the most popular approach for managing data due to their accessibility and performance result capabilities. Examples of RDBMS's include the Amazon RDS, Oracle, and MySQL which all utilize Structured Query Language (SQL) to manipulate the different databases they interact with. All RDBMS's are ACID compliant and typically implement an OLTP system.
To combat the limitations of relational database management systems, NoSQL databases became more popular over the years. The term "NoSQL" was coined by Carlo Strozzi in 1998 as the term for his first database which didn't utilize SQL for managing data, hence the label "NoSQL." Examples of popular NoSQL databases include key-value pair databases, document databases, graph databases, and columnar databases, all of which while are similar in concept are different in theory, as there are advantages and disadvantages to using each in different scenarios.
As we continue to move forward in the technology world, we constantly search for the most optimal solution for all of our data needs. These optimal solutions begin with which database management system or systems we choose to utilize to solve our data-related problems. Some database management systems are more equipped for certain scenarios than others, and figuring out which type works best for you is essential when working with big data.
Most scientists agree that no one really knows how the most advanced algorithms do what they do, nor how well they are doing it. That could be a problem. Advances in synthetic data generation technologies can help. These algorithms generate data with a known ground truth, sufficient volumes and with statistically relevant true and false positives (TP, FP) and true and false negatives (TN, FN) for the nature of the test. AI algorithms can now be measured for precision, c, as the fraction of the predicted matches that are true positive matches, or c = TP/(TP + FP).