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The concept of a kernel in machine learning might initially sound perplexing, but its a fundamental idea that underlies many powerful algorithms. There are mathematical theorems that support the working principle of all automation systems that make up a large part of our daily lives.

Kernels in machine learning serve as a bridge between linear and nonlinear transformations. They enable algorithms to work with data that doesnt exhibit linear separability in its original form. Think of kernels as mathematical functions that take in data points and output their relationships in a higher-dimensional space. This allows algorithms to uncover intricate patterns that would be otherwise overlooked.

So how can you use kernel in machine learning for your own algorithm? Which type should you prefer? What do these choices change in your machine learning algorithm? Lets take a closer look.

At its core, a kernel is a function that computes the similarity between two data points. It quantifies how closely related these points are in the feature space. By applying a kernel function, we implicitly transform the data into a higher-dimensional space where it might become linearly separable, even if it wasnt in the original space.

There are several types of kernels, each tailored to specific scenarios:

The linear kernel is the simplest form of kernel in machine learning. It operates by calculating the dot product between two data points. In essence, it measures how aligned these points are in the feature space. This might sound straightforward, but its implications are powerful.

Imagine you have data points in a two-dimensional space. The linear kernel calculates the dot product of the feature values of these points. If the result is high, it signifies that the two points have similar feature values and are likely to belong to the same class. If the result is low, it suggests dissimilarity between the points.

The linear kernels magic lies in its ability to establish a linear decision boundary in the original feature space. Its effective when your data can be separated by a straight line. However, when data isnt linearly separable, thats where other kernels come into play.

The polynomial kernel in machine learning introduces a layer of complexity by applying polynomial transformations to the data points. Its designed to handle situations where a simple linear separation isnt sufficient.

Imagine you have a scatter plot of data points that cant be separated by a straight line. Applying a polynomial kernel might transform these points into a higher-dimensional space, introducing curvature. This transformation can create intricate decision boundaries that fit the data better.

For example, in a two-dimensional space, a polynomial kernel of degree 2 would generate new features like x^2, y^2, and xy. These new features can capture relationships that werent evident in the original space. As a result, the algorithm can find a curved boundary that separates classes effectively.

The Radial Basis Function (RBF) kernel in machine learning is one of the most widely used kernels in the training of algorithms. It capitalizes on the concept of similarity by creating a measure based on Gaussian distributions.

Imagine data points scattered in space. The RBF kernel computes the similarity between two points by treating them as centers of Gaussian distributions. If two points are close, their Gaussian distributions will overlap significantly, indicating high similarity. If they are far apart, the overlap will be minimal.

This notion of similarity is powerful in capturing complex patterns in data. In cases where data points are related but not linearly separable, the usage of RBF kernel in machine learning can transform them into a space where they become more distinguishable.

The sigmoid kernel in machine learning serves a unique purpose its used for transforming data into a space where linear separation becomes feasible. This is particularly handy when youre dealing with data that cant be separated by a straight line in its original form.

Imagine data points that cant be divided into classes using a linear boundary. The sigmoid kernel comes to the rescue by mapping these points into a higher-dimensional space using a sigmoid function. In this transformed space, a linear boundary might be sufficient to separate the classes effectively.

The sigmoid kernels transformation can be thought of as bending and shaping the data in a way that simplifies classification. However, its important to note that while the usage of a sigmoid kernel in machine learning can be useful, it might not be as commonly employed as the linear, polynomial, or RBF kernels.

Kernels are the heart of many machine learning algorithms, allowing them to work with nonlinear and complex data. The linear kernel suits cases where a straight line can separate classes. The polynomial kernel adds complexity by introducing polynomial transformations. The RBF kernel measures similarity based on Gaussian distributions, excelling in capturing intricate patterns. Lastly, the sigmoid kernel transforms data to enable linear separation when it wasnt feasible before. By understanding these kernels, data scientists can choose the right tool to unlock patterns hidden within data, enhancing the accuracy and performance of their models.

Kernels, the unsung heroes of AI and machine learning, wield their transformative magic through algorithms like Support Vector Machines (SVM). This article takes you on a journey through the intricate dance of kernels and SVMs, revealing how they collaboratively tackle the conundrum of nonlinear data separation.

Support Vector Machines, a category of supervised learning algorithms, have garnered immense popularity for their prowess in classification and regression tasks. At their core, SVMs aim to find the optimal decision boundary that maximizes the margin between different classes in the data.

Traditionally, SVMs are employed in a linear setting, where a straight line can cleanly separate the data points into distinct classes. However, the real world isnt always so obliging, and data often exhibits complexities that defy a simple linear separation.

This is where kernels come into play, ushering SVMs into the realm of nonlinear data. Kernels provide SVMs with the ability to project the data into a higher-dimensional space where nonlinear relationships become more evident.

The transformation accomplished by kernels extends SVMs capabilities beyond linear boundaries, allowing them to navigate complex data landscapes.

Lets walk through the process of using kernels with SVMs to harness their full potential.

Imagine youre working with data points on a two-dimensional plane. In a linearly separable scenario, a straight line can effectively divide the data into different classes. Here, a standard linear SVM suffices, and no kernel is needed.

However, not all data is amenable to linear separation. Consider a scenario where the data points are intertwined, making a linear boundary inadequate. This is where kernel in machine learning step in to save the day.

You have a variety of kernels at your disposal, each suited for specific situations. Lets take the Radial Basis Function (RBF) kernel as an example. This kernel calculates the similarity between data points based on Gaussian distributions.

By applying the RBF kernel, you transform the data into a higher-dimensional space where previously hidden relationships are revealed.

In this higher-dimensional space, SVMs can now establish a linear decision boundary that effectively separates the classes. Whats remarkable is that this linear boundary in the transformed space corresponds to a nonlinear boundary in the original data space. Its like bending and molding reality to fit your needs.

Kernels bring more than just visual elegance to the table. They enhance SVMs in several crucial ways:

Handling complexity: Kernel in machine learning enables SVMs to handle data that defies linear separation. This is invaluable in real-world scenarios where data rarely conforms to simplistic structures.

Unleashing insights: By projecting data into higher-dimensional spaces, kernels can unveil intricate relationships and patterns that were previously hidden. This leads to more accurate and robust models.

Flexible decision boundaries: Kernel in machine learning grants the flexibility to create complex decision boundaries, accommodating the nuances of the data distribution. This flexibility allows for capturing even the most intricate class divisions.

Kernel in machine learning is like a hidden gem. They unveil the latent potential of data by revealing intricate relationships that may not be apparent in their original form. By enabling algorithms to perform nonlinear transformations effortlessly, kernels elevate the capabilities of machine learning models.

Understanding kernels empowers data scientists to tackle complex problems across domains, driving innovation and progress in the field. As we journey further into machine learning, lets remember that kernels are the key to unlocking hidden patterns and unraveling the mysteries within data.

Featured image credit: rawpixel.com/Freepik.

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What Is Kernel In Machine Learning And How To Use It? - Dataconomy

Reddit has introduced Keyword Suggestions, a tool for advertisers that applies machine learning to help expand their keyword lists recommending relevant and targetable keywords, while filtering out keywords that arent brand suitable.

The new system is available via the Reddit Ads Manager and ranks each suggestion by monthly views, and opens up an expanded list of relevant targeting possibilities to increase the reach and efficiency of campaigns.

The tool is powered by advanced machine learning and natural language processing to find the most relevant terms.

This technology takes the original context of each keyword into consideration so that only those existing in a brand-safe and suitable environment are served to advertisers.

In practice, this means machine learning is doing the heavy lifting, pulling from the Reddit posts and conversations that best match each advertisers specific needs. Most importantly, this allows advertisers to show the most relevant ads to the Reddit users who will be most interested in them.

The promise and potential of artificial intelligence, while exciting, has also elevated the value of real, human interactions and interests for both consumers and marketers. As we enter a new chapter in our industry and evolve beyond traditional signals, interest-based, contextually relevant targeting will be the most effective way to reach people where theyre most engaged, said Jim Squires, Reddits EVP of business marketing and growth.

Powered by Reddits vast community of communities, which are segmented by interest and populated with highly engaged discussions, Keyword Suggestions leverages the richness of conversation on Reddit and provides advertisers with recommendations to easily and effectively target relevant audiences on our platform.

The platform has also boosted its interest-based targeting tools with twice the number of categories available for targeting.

Reddits continued focus on enhancing their targeting products via machine learning will certainly help advertisers reach more of their target audience and discover new audiences on the platform. Additionally, implementing negative keyword targeting strategies overall increases relevancy and improves performance, said GroupM vice president and global head of social, Amanda Grant.

Given the rich nature of conversations on the Reddit platform, we expect improved business outcomes as we tap into these tools to refine our focus on the right audience.

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Reddit Expands Machine Learning Tools To Help Advertisers Find ... - B&T

Obnoxious. Annoying. Disrespectful. Inexperienced. Conspiratorial.

Those are just a few of the adjectives one could use to describe Republican upstart Vivek Ramaswamy at the first GOP debate of the 2024 presidential election.

It didnt take long for the relatively unknown businessman-turned-candidate to make his presence known, earning applause and cheers early on, but hardly ingratiating himself to his fellow opponents.

He sparred with many, and seemed to revel in the spotlight, but, to what end? Was this a real star turn that could position Ramaswamy to actually vie for the nomination? Or was it just another attention-seeking performance meant to make him Fox-famous, the likes of which seem to define the new American right?

While all of the descriptors I used above annoying, obnoxious, conspiratorial, etc. are hugely off-putting for moderate, issues-based conservatives like me who are desperate to move past Trumpism, tribalism, denialism and demagoguery, we are clearly not the audience. If Ramaswamys audience was MAGA world, it seems he put on the perfect show.

He adopted Donald Trumps pugnacity he was cocky, aggressive, and dismissive of his competitors, baselessly calling them all bought and paid for, suggesting former Gov. Chris Christie was just there to become an MSNBC contributor, and that former Gov. Nikki Haley was destined to be on the board of defense contractors Lockheed Martin and Raytheon. He frequently feigned confusion over whatever former Vice President Pence whom he called Mike more than once had just said.

Their obvious exasperation over Ramaswamys insults, interruptions, and gaslighting recalled the 2016 Republican primary, where seasoned politicians like former Gov. Jeb Bush, Sen. Marco Rubio, Sen. Rand Paul and others had to dodge and endure Trumps petulant wrench-throwing while hoping to run serious campaigns.

They managed to land some good one-liners against him, though. Pence said now wasnt the time to elect a rookie, Christie joked that Ramaswamy sounded like ChatGPT, while Haley declared definitively, You have no foreign policy experience and it shows.

Like Trump, Ramaswamy also courted quackery. Fresh off some controversial comments he made about Jan. 6 and Sept. 11 maybe being inside jobs, he sprinkled in some other conspiracies about climate change, and also went where crank voices like Alex Jones and Lara Logan have gone in rooting for Putin in Ukraine.

And he did the most important part of the job to please MAGA, which was to go to cartoonish lengths to suck up to Trump, at one point calling him the best president of the 21st century and demanding his opponents join him in pledging to pardon Trump if hes convicted on any of the 90-plus charges hes facing.

Which begs the question, of course, if Trump is so great and must be protected at all costs, why are you running against him, then?

Thats what leads to the assumption that, as Christie said on stage, Vivek just wants to be famous. Afterwards he went on ABC News and called the debate an unambiguous success for him.

Trump agreed, posting his appreciation for Ramaswamys obsequious words:

This answer gave Vivek Ramaswamy a big WIN in the debate because of a thing called TRUTH. Thank you Vivek!

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Catch up on the days top five stories every weekday afternoon.

By these metrics getting attention and especially Trumps Ramaswamy certainly won. What he won, though, was unclear.

Most headlines focused on his ability to take center stage, grab the spotlight, and break through, admittedly important things for a newcomer to do.

And in a political environment like the new Republican Party, where attention, celebrity, and sucking up to Trump are much more valuable currency than things like experience, seriousness, and competence, the rapping, topless tennis-playing, conspiracy-theorizing Ramaswamy is well-positioned to go far in this primary.

But how far does he want to go? Does he want to beat his idol? Trump wont allow that, presumably. Or is he secretly hoping Trump goes to prison so he can step into his shoes and become the nominee? Does he then truly believe his extremist positions and unserious campaign antics could win in a general election? (Hey, its been done before.)

Or, more likely, is he just using the American democratic process to line his own pockets, sell books and merch, see his name splashed across a media he insists is corrupt, and ultimately land a cushy job alongside Jesse Watters and Jeanine Pirro on Fox, where hell get paid to professionally push lies and conspiracy theories to an unwitting audience?

In this Republican Party, few things still surprise. While he wont have my vote, based on his performance Wednesday night Vivek Ramaswamy makes a terrific MAGA candidate unqualified, unserious, unlikableand therefore utterly electable.

secuppdailynews@gmail.com

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Vivek was unbearable, so his future in GOP is bright - New York Daily News

A few weeks ago, Politicos Florida Playbook ran a story revealing what hundreds of people, groups, and journalists in the state were asking for: texts, emails, calendars, letters, and receipts to or from Floridas governor, presidential candidate Ron DeSantis.

In Politicos telling, many of the requesters were affiliated with the Democratic Party, making demands under the states open-records law to get damning material against political enemies. And, in DeSantis case, Floridas public records certainly marked the best starting point to look for muck. The list that Politico received of requesters targeting DeSantis in his home state was 222 pages long.

Unsurprisingly, mostly Democratic-aligned groups asked for dirt on DeSantis and his inner circle, Politico wrote. Oddly, no one tied to Trump or other 2024 candidates asked for such records, though its possible that GOP campaigns used an untraceable proxy to avoid angering a future Republican president.

Why didnt the Trump campaign file such requests? Steven Cheung, a Trump campaign spokesperson, bluntly told Politico: We have information that no opposition researcher can ever find.

Opposition research oppo in the vernacular of politicians is a basic building block in every political campaign. Oppo is the ammunition behind every negative campaign ad; every gut punch in a debate. The higher the stakes, the deeper the research. And many more players are in the opposition-research game now, following the Supreme Courts 2010 decision in Citizens United v. FEC.

The Courts 5-to-4 decision in Citizens United opened the door to unlimited election spending by so-called independent political action committees, aka Super PACs. Super PACs spend big on negative ads, and oppo is their ammo. According to OpenSecrets.org, which tracks the flow of money in politics, spending in the 2020 presidential and congressional races totaled $14.4 billion, more than double the total cost of the record-breaking 2016 presidential election cycle. That huge influx of money led to more negative campaign advertisements across all media, which in turn juiced the need for more opposition research. (Full disclosure: I am a longtime board member of OpenSecrets).

Not surprisingly, those conducting opposition research turn early and often to the Freedom of Information Act. So we decided to dig into PoliScio Analytics competitive-intelligence database FOIAengine, which tracks FOIA requests in as close to real-time as their availability allows, to see what the players are up to.

With so many candidates vying for the Republican nomination, the Republican National Committee is staying on the sidelines, leaving opposition research to the affiliated PACs and super PACs of the various candidates. Next week, well take a closer look at some of the thousands of FOIA requests from Republican proxies acting on behalf of, or in synch with, the Republican candidates.

With the Democrats, its the GOP story in reverse. The Democrats know who their probable standard bearers will be. But, with more than a dozen declared Republican candidates and an even greater number of undeclared long shots, Democratic oppo researchers must throw a dragnet, systematically spreading an array of FOIA requests across a broad swath of agencies and departments.

President Bidens main super PAC, Future Forward, which spent more than $130 million in 2020, doesnt show up as a requester in FOIAengine at all. Instead, the Democratic National Committee appears to be taking the oppo-research lead. According to FOIAengine, the DNC has filed more than 300 recent FOIA requests with federal agencies, covering the wide range of candidates who could end up as the eventual presidential or vice presidential nominee on the Republican presidential ticket.

FOIA requests to the federal government can be an important early warning of bad publicity, litigation to come, or uncertainties that must be hedged or gamed out. In this case, the DNCs FOIA requests appear to reflect a calculus that even if the race for the top of the ticket is settled early, the vice-presidential spot will end up being a wild card. Hence, the DNC must place a lot of early bets on the table.

Over the past year or so, the Democrats have filed extensive FOIA requests with various federal agencies seeking detailed information on at least 19 present or former Republican officeholders. The list includes some who have stated flatly that theyre not running for president, but who could end up as a running mate. There are some dark horses: Sen. Rand Paul (R-Ky.), Mike Pompeo, Ben Carson, and Gov. Glenn Youngkin (R-Va.) are among the DNCs targets. And a few surprises: Rep. Elise Stefanik (R-N.Y.) and Sen. Joni Ernst (R-Iowa) are on the DNCs list; Vivek Ramaswamy isnt yet.

Following are highlights from the DNCs opposition-research FOIA requests thus far:

To see all the DNC opposition-research requests, log in or sign up to become a FOIAengine beta user.

Next: Thousands of opposition-research requests from Republican-affiliated PACs.

John A. Jenkins, co-creator of FOIAengine, is a Washington journalist and publisher whose work has appeared in The New York Times Magazine, GQ, and elsewhere. He is a four-time recipient of the American Bar Associations Gavel Award Certificate of Merit for his legal reporting and analysis. His most recent book is The Partisan: The Life of William Rehnquist. Jenkins founded Law Street Media in 2013. Prior to that, he was President of CQ Press, the textbook and reference publishing enterprise of Congressional Quarterly. FOIAengine is a product of PoliScio Analytics (PoliScio.com), a new venture specializing in U.S. political and governmental research, co-founded by Jenkins and Washington lawyer Randy Miller. Learn more about FOIAengine here. To review FOIA requests mentioned in this article, subscribe to FOIAengine.

Write to John A. Jenkins at JAJ@PoliScio.com.

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The DNC Goes Fishing What Will it Catch? - Law Street Media

But in the wake of Ohio voters swatting away a recent Republican effort to make it much harder to amend the state constitution and build a roadblock in front of an effort to enshrine abortion rights protections into the state constitution this November theres been a bit of buzz about the reemergence of Ohio as a key presidential battleground.

Dont bet on it.

For starters, ballot issues are not partisan elections, and keep in mind Democratic issue positions can be more popular than Democratic candidates. That was very likely the case in the Aug. 8 Issue 1 vote in Ohio, as a broad coalition of Democrats, independents, and even some Republican voters gave a big thumbs down to a Republican effort to neuter voters power to amend the states constitution.

In 2018, we saw a similar phenomenon when Missouri voters approved a minimum wage ballot issue by 25 points in the same election that they backed now-Republican Sen. Josh Hawley over then-Democratic incumbent Claire McCaskill by 6 points. Just last year, Kentuckians voted with the pro-abortion rights side, declining by about 5 points to specify that the Kentucky Constitution does not contain abortion rights protections. In the same election, they reelected Republican Sen. Rand Paul by 24 points. Be careful about extrapolating trends from an off-year ballot issue vote in Ohio to next years general election.

The unpopularity of Issue 1 was seen in almost every corner of the state. Some of the counties that stood out were the 15 collar counties that touch the states three major urban counties: Cuyahoga (Cleveland), Franklin (Columbus), and Hamilton (Cincinnati). The Issue 1 vote, which saw No (the Democratic position) beat Yes (the Republican position) by 14 points, was 20 points bluer than the 6-point margin enjoyed by Republican Sen. J.D. Vance in his victory over former Democratic Rep. Tim Ryan last year. In almost all of these collar counties, the No side ran ahead of that 20-point difference. One could argue that this is a leading indicator of a blue trend in these places.

But the actual partisan trends in the collar counties have been different and much less encouraging for Democrats.

Broadly speaking, the story of the Trump era in the Industrial North has been one of eroding Democratic presidential performance. Relative to the nation, only Illinois and Minnesota were roughly as Democratic in 2020 as they were in 2012. All of the other states in the region that Barack Obama carried at least once (Indiana, Iowa, Michigan, Ohio, Pennsylvania and Wisconsin) have gotten more Republican.

The regional movement toward the GOP at the presidential level is the upside of the Trumpian realignment, as he has pulled more white voters who generally do not have a four-year college degree into the GOP coalition a vital and large bloc in many of these states.

Amid the larger, pro-Republican trends in the Industrial North, there has been some pro-Democratic movement in the regions collar counties movement that was vital in Joe Bidens efforts to reclaim some of these states in 2020. For instance, some of the still-red Milwaukee collar counties Waukesha and Ozaukee, two of the states three so-called WOW counties both saw their GOP presidential margins drop double-digits from 2012 to 2020. In Michigan, the Detroit satellites of Oakland and Washtenaw counties got bluer, and in southeast Pennsylvania, the Democratic margin in Philadelphias northwest neighbor, Montgomery County, nearly doubled. Overall, the trade-offs were still good for Trump in this region, but his erosion from Mitt Romneys performance in some key suburban places contributed to his narrow losses in the old Blue Wall in 2020.

In Ohio, however, its much harder to find examples of Democratic growth in these kinds of suburban/exurban collar counties: 12 of the 15 that touch Cuyahoga, Franklin or Hamilton got redder from 2012 to 2020. The sole exceptions were Delaware, north of Columbus, and Warren and Butler, north of Cincinnati. Delaware has by far the highest four-year college attainment of any county in Ohio but it is still Republican, voting for Trump by 7 points, down from Romneys 23. Meanwhile, in the vast swaths of small town and rural Ohio, the Democrats have collapsed: The Republican presidential nominee went from winning just six of Ohios 88 counties with 70 percent or more of the vote in 2012 to exactly half of them in 2020.

The basic statewide story that helps explain both the sticky Republicanism of the collar counties as well as the big GOP movement in much of the rest of the state is that prior to the Trump realignment, Democrats stayed afloat in Ohio in large part because non-college whites there were less Republican than they were nationally.

But in 2016, according to a detailed and respected report from the liberal Center for American Progress comparing the 2012 election with 2016, non-college whites in Ohio actually became slightly more Republican than they were nationally, while the states college whites also remained more Republican than they were nationally. The AP/Fox News VoteCast exit poll found this same basic alignment in 2020. This combination a Republican stampede among non-college whites, paired with a college white group that retains a GOP lean is electorally deadly for Democrats in Ohio, particularly because the state is whiter than the nation as a whole.

If theres a silver lining for Democrats in these otherwise troubling trendlines, Issue 1 did provide a template for what a future Democratic victory in Ohio might look like, with the suburban/exurban collar counties either voting Democratic or at least not giving the GOP the landslide margins to which they have become accustomed.

That could matter a great deal in the 2024 battle for control of the Senate. Democratic incumbent Sherrod Browns reelection bid likely depends on finding some new votes in these places, as it seems reasonable to expect that he will continue to lose ground in rapidly reddening eastern Ohio in a presidential year. One telling sign is that even though No won by 14 points in 2023, and Brown won by 7 in his 2018 reelection, Browns margin was better than Nos in much of the region, including in Mahoning and Trumbull counties, home to the post-industrial cities of Youngstown and Warren, respectively, and poster children for the Trump realignment. This perhaps suggests that the continued erosion for Democrats in these one-time cobalt blue counties will only continue.

If Ohio did in fact vote Democratic for president in 2024, it would likely be as part of much wider improvement for the party across the region, such that Michigan, Pennsylvania and Wisconsin would be voting Democratic by several points apiece and the Democrats would likely be winning the presidency easily. Thats possible, of course, but the odds are against it.

Ohio is, rightly, going to remain a focus in 2023, with a looming vote coming on abortion rights in November. But regardless of what happens in that ballot measure, the Trump realignment ended at least for now the states defining role as a presidential bellwether.

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No, Ohio Is Not in Play - POLITICO - POLITICO