Technology Articles

Article / Updated 09-24-2024

Both linear and logistic regression see a lot of use in data science but are commonly used for different kinds of problems. You need to know and understand both types of regression to perform a full range of data science tasks. Of the two, logistic regression is harder to understand in many respects because it necessarily uses a more complex equation model. The following information gives you a basic overview of how linear and logistic regression differ. The equation model Any discussion of the difference between linear and logistic regression must start with the underlying equation model. The equation for linear regression is straightforward. y = a + bx You may see this equation in other forms and you may see it called ordinary least squares regression, but the essential concept is always the same. Depending on the source you use, some of the equations used to express logistic regression can become downright terrifying unless you’re a math major. However, the start of this discussion can use one of the simplest views of logistic regression: p = f(a + bx) >p, is equal to the logistic function, f, applied to two model parameters, a and b, and one explanatory variable, x. When you look at this particular model, you see that it really isn’t all that different from the linear regression model, except that you now feed the result of the linear regression through the logistic function to obtain the required curve. The output (dependent variable) is a probability ranging from 0 (not going to happen) to 1 (definitely will happen), or a categorization that says something is either part of the category or not part of the category. (You can also perform multiclass categorization, but focus on the binary response for now.) The best way to view the difference between linear regression output and logistic regression output is to say that the following: Linear regression is continuous. A continuous value can take any value within a specified interval (range) of values. For example, no matter how closely the height of two individuals matches, you can always find someone whose height fits between those two individuals. Examples of continuous values include: Height Weight Waist size Logistic regression is discrete. A discrete value has specific values that it can assume. For example, a hospital can admit only a specific number of patients in a given day. You can’t admit half a patient (at least, not alive). Examples of discrete values include: Number of people at the fair Number of jellybeans in the jar Colors of automobiles produced by a vendor The logistic function Of course, now you need to know about the logistic function. You can find a variety of forms of this function as well, but here’s the easiest one to understand: f(x) = e^x / e^x + 1 You already know about f, which is the logistic function, and x equals the algorithm you want to use, which is a + bx in this case. That leaves e, which is the natural logarithm and has an irrational value of 2.718, for the sake of discussion (check out a better approximation of the whole value). Another way you see this function expressed is f(x) = 1 / (1 + e^-x) Both forms are correct, but the first form is easier to use. Consider a simple problem in which a, the y-intercept, is 0, and ">b, the slope, is 1. The example uses x values from –6 to 6. Consequently, the first f(x) value would look like this when calculated (all values are rounded): (1) e^-6 / (1 + e^-6) (2) 0.00248 / 1 + 0.00248 (3) 0.002474 As you might expect, an xvalue of 0 would result in an f(x) value of 0.5, and an x value of 6 would result in an f(x) value of 0.9975. Obviously, a linear regression would show different results for precisely the same x values. If you calculate and plot all the results from both logistic and linear regression using the following code, you receive a plot like the one below. import matplotlib.pyplot as plt %matplotlib inline from math import exp x_values = range(-6, 7) lin_values = [(0 + 1*x) / 13 for x in range(0, 13)] log_values = [exp(0 + 1*x) / (1 + exp(0 + 1*x)) for x in x_values] plt.plot(x_values, lin_values, 'b-^') plt.plot(x_values, log_values, 'g-*') plt.legend(['Linear', 'Logistic']) plt.show() This example relies on list comprehension to calculate the values because it makes the calculations clearer. The linear regression uses a different numeric range because you must normalize the values to appear in the 0 to 1 range for comparison. This is also why you divide the calculated values by 13. The exp(x) call used for the logistic regression raises e to the power of x, e^x, as needed for the logistic function. The model discussed here is simplified, and some math majors out there are probably throwing a temper tantrum of the most profound proportions right now. The Python or R package you use will actually take care of the math in the background, so really, what you need to know is how the math works at a basic level so that you can understand how to use the packages. This section provides what you need to use the packages. However, if you insist on carrying out the calculations the old way, chalk to chalkboard, you’ll likely need a lot more information. The problems that logistic regression solves You can separate logistic regression into several categories. The first is simple logistic regression, in which you have one dependent variable and one independent variable, much as you see in simple linear regression. However, because of how you calculate the logistic regression, you can expect only two kinds of output: Classification: Decides between two available outcomes, such as male or female, yes or no, or high or low. The outcome is dependent on which side of the line a particular data point falls. Probability: Determines the probability that something is true or false. The values true and false can have specific meanings. For example, you might want to know the probability that a particular apple will be yellow or red based on the presence of yellow and red apples in a bin. Fit the curve As part of understanding the difference between linear and logistic regression, consider this grade prediction problem, which lends itself well to linear regression. In the following code, you see the effect of trying to use logistic regression with that data: x1 = range(0,9) y1 = (0.25, 0.33, 0.41, 0.53, 0.59, 0.70, 0.78, 0.86, 0.98) plt.scatter(x1, y1, c='r') lin_values = [0.242 + 0.0933*x for x in x1] log_values = [exp(0.242 + .9033*x) / (1 + exp(0.242 + .9033*x)) for x in range(-4, 5)] plt.plot(x1, lin_values, 'b-^') plt.plot(x1, log_values, 'g-*') plt.legend(['Linear', 'Logistic', 'Org Data']) plt.show() The example has undergone a few changes to make it easier to see precisely what is happening. It relies on the same data that was converted from questions answered correctly on the exam to a percentage. If you have 100 questions and you answer 25 of them correctly, you have answered 25 percent (0.25) of them correctly. The values are normalized to produce values between 0 and 1 percent. As you can see from the image above, the linear regression follows the data points closely. The logistic regression doesn’t. However, logistic regression often is the correct choice when the data points naturally follow the logistic curve, which happens far more often than you might think. You must use the technique that fits your data best, which means using linear regression in this case. A pass/fail example An essential point to remember is that logistic regression works best for probability and classification. Consider that points on an exam ultimately predict passing or failing the course. If you get a certain percentage of the answers correct, you pass, but you fail otherwise. The following code considers the same data used for the example above, but converts it to a pass/fail list. When a student gets at least 70 percent of the questions correct, success is assured. y2 = [0 if x < 0.70 else 1 for x in y1] plt.scatter(x1, y2, c='r') lin_values = [0.242 + 0.0933*x for x in x1] log_values = [exp(0.242 + .9033*x) / (1 + exp(0.242 + .9033*x)) for x in range(-4, 5)] plt.plot(x1, lin_values, 'b-^') plt.plot(x1, log_values, 'g-*') plt.legend(['Linear', 'Logistic', 'Org Data']) plt.show() This is an example of how you can use list comprehensions in Python to obtain a required dataset or data transformation. The list comprehension for y2 starts with the continuous data in y1 and turns it into discrete data. Note that the example uses precisely the same equations as before. All that has changed is the manner in which you view the data, as you can see below. Because of the change in the data, linear regression is no longer the option to choose. Instead, you use logistic regression to fit the data. Take into account that this example really hasn’t done any sort of analysis to optimize the results. The logistic regression fits the data even better if you do so.

Cheat Sheet / Updated 09-16-2024

Apple Vision Pro is a mixed-reality headset that fuses augmented reality (where you can see digital information overlaid on top of the real world around you) with virtual reality (realistic imagery that fully envelops your field of vision). You don’t need a mouse and keyboard with Apple Vision Pro — you can control everything with your eyes, voice, and hands in the air. This Cheat Sheet shows you how to use gestures to control Apple Vision Pro, how to capture spatial photos and videos, and more tips for getting the most out of Apple Vision Pro.

Cheat Sheet / Updated 09-16-2024

The Marketing with AI For Dummies book, by Shiv Singh, offers great advice for using artificial intelligence (AI) in all aspects of marketing efforts. In the book, marketers at any level can find solid guidance for applying the capabilities of AI, whether they want to develop entire marketing campaigns or simply find help for automating repetitive processes. In this Cheat Sheet, find information about planning successful AI implementations, training marketing teams to use AI tools, finding the right partners for your work with AI, and avoiding over-reliance on AI automation.

Article / Updated 09-11-2024

Android Auto is a new feature available for your Droid smartphone. Because cellphone-related car accidents are on the rise, many major automobile manufacturers have decided to implement a safer way to use your smartphone while driving. Once your Droid is connected to your automobile, the phone’s screen will be mirrored on the display of your car stereo. Credit: Image courtesy of Android.com Android Auto gives you a hands-free option for operating your smartphone while driving. What do you need to use Android Auto? Before you can use the new Android Auto safety feature, you will need to take a couple of things into consideration. Do you have an Android smartphone running OS 5.0 or higher? The Android Auto safety feature is only available on 5.0 (Lollipop) and higher. If your smartphone is running an older Android OS, unfortunately, you will not be able to use Android Auto. Update your device (if applicable) to Android 5.0+. If your device is unable to update to Lollipop and you want this new feature, a new phone purchase may be required. Do you have a data plan through your cellphone provider? Because Android Auto uses data-rich applications such as the voice assistant Google Now (Ok Google) Google Maps, and many third-party music streaming applications, it is necessary for you to have a data plan. An unlimited data plan is the best way to avoid any surprise charges on your wireless bill. Do you have a compatible car or supported aftermarket stereo? Many 2016 automobile models and aftermarket stereos will support Android Auto. If a new car purchase is not in your near future, an aftermarket stereo is a great option. Check the Android Auto page to see if the car or stereo you wish to purchase is supported. What is included with Android Auto? The built in applications supported by Android Auto are listed as follows: Google Maps: The Google Maps app offers directions, road and traffic conditions, as well as travel time. Get directions quickly and accurately. Messages: The Messages application will read incoming text messages. You speak your response into the car stereo/microphone. Android translates your speech into a text and sends it to your recipient. Messages paired with Android Auto allow you to keep your eyes on the road instead of on your Droid. Music: The Music app allows access to the stored music on your smartphone. You can rock out to all your favorite jams directly from the car stereo. Home: Home is a shortcut icon that takes you directly to your Home screen. Here, you can quickly see all of your supported applications and safely make your selection while driving. Phone: The Phone app, paired with Android Auto, allows you to make and receive calls through your car stereo. When a call is coming in, your music or audio pauses and you will hear the ringtone instead. Take or reject the call and the music resumes as soon as you are finished. Applications such as Pandora, Spotify, Google Play Music, and many more are supported. Search the Google Play Store for additional applications. To access other supported applications while your phone is connected to Android Auto, you simply download the applications to your smartphone and coordinating icons will display on your car stereo. If no extra icons are displayed then no supported applications have been downloaded. Keep in mind that Android Auto is not an OS built into your car stereo, but is a feature that allows your phone to display on the screen in your car. Most Android Auto-supported stereos also support Apple CarPlay.

Step by Step / Updated 08-27-2024

Windows usually detects the presence of a network adapter automatically; typically, you don’t have to install device drivers manually for the adapter. When Windows detects a network adapter, Windows automatically creates a network connection and configures it to support basic networking protocols. You may need to change the configuration of a network connection manually, however. The following steps show you how to configure your network adapter on a Windows 10 system:

Step by Step / Updated 08-27-2024

Traditional Word users may be really disappointed that pressing the Ctrl+F key in Word 2013 summons the Navigation pane. They want Ctrl+F to bring forth the traditional Find dialog box, the one that’s now called the Advanced Find dialog box. To make that happen, follow these steps:

Article / Updated 08-19-2024

The landscape of contract lifecycle management (CLM) is rapidly evolving with the advent of advanced technologies like generative AI (Gen AI). Gen AI is a new iteration of AI whose key benefit is the generation of new content based on the patterns and information it’s learned from existing datasets. Gen AI isn’t a trend or a fad. It’s a new technology that represents a seismic shift in many ways. Organizations are no longer asking if they should embrace AI in CLM but rather how swiftly and effectively they can adapt. The golden age of powerful intelligent technology must be embraced, and you must adapt to advance your business. Integrating these technologies into your CLM can make your CLM an even more powerful tool. AI is like giving machines a brain to think and learn, while Gen AI is about giving them creativity to make new things. When you apply Gen AI to CLM and your contracting processes, it truly expedites your third-party paper review, contract redlining, playbook review, negotiation, and more. In this article, you discover how Gen AI’s powerful use cases are wielded in CLM. Tackling Gen AI Use Cases that Impact CLM Gen AI streamlines contract creation, analysis, and risk assessment, revolutionizing how businesses manage contracts. It’s an exciting development that promises efficiency and accuracy in CLM processes. Within CLM, Gen AI’s prominent use cases include the following: Drafting your contracts with ease: Transform how your organization handles your contracts and their processes. Creating contracts through traditional methods is a time-consuming process that requires highly trained experts, but Gen AI can flip that old way of doing things and start automating your contract drafting. Gen AI does this by learning from your existing contracts and then generating new ones based on your specific business needs and specific inputs that you provide to the tool. Improved adoption: Gen AI becomes a critical co-pilot, working with your users without requiring training. By adding this resource capacity, you can increase efficiency through automating repetitive processes, such as expedited contract review and risk analysis. Your business can do more and free up valuable human resources to focus on strategic initiatives. While Gen AI is still new and slowly being adopted, the benefits are compelling for businesses to adopt Gen AI faster. Voice and text-activated operation: You can easily communicate your objectives through voice commands or by typing, and Gen AI provides guided, click-free actions to efficiently achieve your goals. Intelligent search: Gen AI is able to review large amounts of data quicker than before, allowing for less time spent on searches and more time achieving precise results faster. It can identify key provisions and the existence of specific business terms across agreements swiftly, making audits or merger and acquisitions (M&A) transactions much easier. Advanced business intelligence: Gen AI offers more robust contextual insights and actionable recommendations, including summaries of data that it then can use to drive more data-driven decisions. These AI insights can help you negotiate better terms, optimize contract structures, and align legal strategies with broader business objectives. Proactive support and risk management: Gen AI facilitates smooth collaboration during document review, and it can proactively identify legal risks, offering recommendations to ensure compliance and mitigate potential issues. In today’s culture, minimizing risk and ensuring compliance are paramount. Gen AI can leverage advanced algorithms to systematically analyze agreements, flag potential compliance issues, and ensure adherence to legal standards. With Gen AI’s contract analysis and risk assessment, your organization can make better informed decisions about its contracts. Using Gen AI Use Cases to Strengthen Your Teams AI-powered CLM use cases provide value in diverse scenarios. By implementing AI contract software, all your teams benefit: Legal: Legal departments can automate contract analysis, strategy development, and negotiations. AI also ensures that contracts comply with the latest legal standards and regulations. Procurement: Procurement teams can automate the vendor contract lifecycle and third-party paper reviews. AI streamlines the creation, review, and approval of contracts, ensuring that procurement processes are seamless and compliant. Sales: Sales teams leverage AI to accelerate the contract negotiation process. By expediting redlining and ensuring the accuracy of contract terms, sales professionals can close deals more efficiently and with reduced risks. Compliance: AI helps you monitor and ensure adherence to contractual obligations. By providing real-time insights into contract performance, AI-enhanced solutions help identify and mitigate risks associated with non-compliance. Expanding Gen AI in CLM with Malbek You’re ready to elevate your CLM experience and unleash the power of Gen AI. You want to maximize the power of your digital contracts, but you need a solid partner along the way. In this section, you learn more about Malbek and how the company can help you do just that. To learn more about Malbek, you can also visit one of these resources: • www.malbek.io • www.malbek.io/platform Simplify CLM complexity Malbek empowers its customers with a dynamic, centralized, and fully configurable CLM platform that simplifies your CLM processes. CLM can be complex, but with a trusted partner, you can distill critical insights from contracts for actionable decision-making and peak profitability. Accelerate contracting velocity Build and launch contract and approval processes with ease. From intuitive workflows and seamless approvals to swift contract generation, Malbek’s platform empowers enterprises to navigate contracts with unprecedented speed, ensuring efficiency, compliance, and strategic impact at every turn. Unite global teams and improve collaboration Malbek seamlessly integrates with your favorite business apps, such as Salesforce, Microsoft, SAP, NetSuite, Slack, Coupa, OneTrust, Adobe Sign, DocuSign, and more. By connecting your CLM system with the rest of your business, you can maintain a single source of truth and streamline your operations. Improve decision-making and minimize risk Eliminate time-consuming, manual tasks that take away from high-value objectives. With Malbek AI infused throughout the contracting process, you gain immediate access to timely contextual insights and recommendations to have the greatest impact on your business. AI also streamlines negotiations and shortens review cycles. Download your free copy of Contract Lifecycle (CLM) Management For Dummies, Malbek Special Edition today.

Article / Updated 08-19-2024

The need to fortify your digital assets is crystal clear — or at least it should be. Having robust security depends on integrating diverse security protocols. Utilizing a framework like the Capability Maturity Model (CMM) enables your organization to evaluate how well it’s protected and provides a clear path of progression for improving protection. Managing risk and becoming resilient against prevalent cyber dangers is an increasingly complex task. We live in a mega-connected world, and organizations are assessing and improving their governance maturity step by step to ensure a more secure digital environment for everyone. Applying the CMM approach to SAP application security involves strategically integrating governance maturity best practices into every aspect of your SAP system’s management. The governance and compliance life cycle involves the ongoing management and safeguarding against internal and external threats toward your information systems and data. This cycle includes three stages: getting clean, staying clean, and optimizing. The goal of this cycle is to establish and maintain effective access control measures. Utilizing CMM enhances your security measures through more efficient organization for assessing and improving your risk mitigation efforts. Keep reading for a primer on each stage in the governance and compliance life cycle. Getting Clean In the first stage of the governance and compliance life cycle, the goal is to produce a more risk-free environment by creating more visibility into your risk landscape. You do that in two stages: Identify the scope of your application landscape. What does that mean? You take inventory. This assessment starts with creating a record of all applications within your organization. Identify and document all applications you’re currently using, along with the users with access to each system. Identifying technical debt and user access bloat can help streamline focus to critical applications with key user bases. Execute access risk analysis (ARA) to identify and correct risks in any existing access. Look at your existing access rights and permissions for each application. Determine who has access to what and assess if these permissions are appropriate. Keeping sensitive data safe is more important than ever. Conducting this risk analysis ensures that only the right people can access the appropriate data. When you take the time to conduct a thorough ARA, you’ll see many benefits: Enhanced security and authorized access Effective regulatory compliance Optimized resource utilization Improved data integrity Strengthened reputational standing Reduced operational costs Staying Clean After you’ve done the detective work in the getting clean stage, you stay clean by using an automated process. Here, you start implementing preventative risk checks to ensure that you address the potential security threats when people come, go, and move within your business. Streamlining with access request management Streamlining your access request management efforts centralizes access requests, approvals, and auditing — all within a user-friendly interface. After getting the appropriate approvals, you ensure that the right people have access to the right data. Those approvals can include manager review, role or access owner review, and risk owner approval with any necessary mitigating controls applied prior to provisioning access. Validating user access certifications User access certifications ensure that users’ outdated access rights don’t remain. They also maintain the regular review and revalidation of controls and risks so you stay up to date. Strike the right balance between efficiency and effectiveness in application access certifications. Automating, centralizing, and optimizing the certification process reduces the amount of time it takes to complete user access reviews and enhances their accuracy and impact. Optimizing In the last stage, optimizing, you focus on continually improving your environment after establishing a documented, repeatable, and automated risk management process (that’s in the first two stages: getting clean and staying clean). Automating elevated access management processes The automation of elevated access management processes enables you to optimize how sensitive access is requested, provisioned, and monitored. This approach builds on the access risk analysis results to identify sensitive access and enable end-users to request temporary, time-bound checkout of the access. After approvals are received, access is automatically provisioned and deprovisioned in alignment with the approved timeframe, with change logs available for management review. Ensuring that elevated access processes are efficient and consistent helps your company implement improved risk management, gain auditor approval, and improve end-user satisfaction. Monitoring and quantifying risk exposure You can’t eliminate every risk, and you may go crazy trying. Set predefined thresholds for your risk exposure so you can identify the risks that threaten those limits. That way, you’re only tracking and reporting on all quantifiable risks that actually occurred instead of the thousands of risks that never happened, which wastes everyone’s precious time. Executing continuous controls monitoring and risk quantification produces efficient and consistent processes to help save time, increase productivity, lower costs, and implement approved designs. Addressing threat detection Focus on what matters. Security and operations teams often lack visibility and understanding of the data that can indicate potential architecture-level security threats — threats that may harm your critical business applications. But with a continuous monitoring system, you can reap the benefits of threat detection and response capabilities, such as Continuous threat detection coverage for thousands of threat indicators Automatic updates with the latest threat information, patch availability, and ongoing research Rapid response to threats with resolution guidance so you can reduce investigation response times Enriching your security information and event management (SIEM) applications with detailed threat detection data How Pathlock Can Help When dealing with risk, Pathlock provides customers with a comprehensive set of modular capabilities. Designed to seamlessly work together, the available tools reduce potential risk by following the get clean, stay clean, optimize methodology. To learn more about this practice, visit www.pathlock.com/sap. To find out more about Pathlock and SAP application security, check out SAP Application Security For Dummies, Pathlock Special Edition. Head to get.pathlock.com/direct-ebook-sap-application-security-for-dummies-special-edition for your free e-book and start planning your SAP application security strategy to get clean, stay clean, and optimize.

Cheat Sheet / Updated 08-07-2024

Photoshop CS6 retains all it had in previous versions —, and provides new features to help you with your tasks, such as a darker, more immersive, User Interface, true vector Shape layers, the Oil Paint filter, Adaptive Wide Angle correction, Content-Aware Move tool, new brush tips, and more. None of it is hard to learn, and all of it will help enhance both your productivity and creativity.

Cheat Sheet / Updated 07-27-2024

When you're podcasting, you have to keep track of a lot of components. Besides checking that the hardware is operating properly, your software is capturing audio without fail, and you're keeping track of your latest episode’s analytics, you also have to keep straight all the minute details. Ensure that your podcasts are well-received by adhering to technical standards for artwork and audio. Check out some of the podcasting directories where you want to have your podcasts listed. And if you’re doing a podcast interview, a little prep time can save a lot of embarrassment.