Outlier

Using batting stats from Major League Baseball’s 2023 seasonShohei Ohtani, photo by Erik Drost on Flikr, CC BY 2.0Outlier detection is an unsupervised machine learning task to identify anomalies (unusual observations) within a given data set. This task is helpful in many real-world cases where our available dataset is already “contaminated” by anomalies. Scikit-learn implements several outlier detection algorithms, and in cases where we have an uncontaminated baseline, we can also use these algorithms for novelty…

Microsoft on Tuesday accused Britain's anti-trust regulator of being a global "outlier" in blocking its $69 billion takeover of “Call of Duty” maker Activision Blizzard.The Competition and Markets Authority (CMA) vetoed the deal in April, saying it could hurt competition in the nascent cloud gaming market, sparking a furious row. The company is asking for an appeal, in which Activision wants to intervene, to be heard in July. Lawyers representing the CMA said in court filings that Microsoft's appeal should be heard in…

via Warner Bros. Not many franchise in history can lay claim to a 100 percent success rate, but one of the most consistent has been Legendary’s MonsterVerse, even if third chapter Godzilla: King of the Monsters sticks out like a sore thumb. There’s no shame in being the weakest installment in any ongoing mythology, because it’s a title that has to be awarded somewhere down the line, but what makes it all the more intriguing is that the other three blockbuster monster mashes have proven to be remarkably…

Thanks to PCA’s sensitivity, it can be used to detect outliers in multivariate datasets.Photo by Andrew Ridley on UnsplashPrincipal Component Analysis (PCA) is a widely used technique for dimensionality reduction while preserving relevant information. Due to its sensitivity, it can also be used to detect outliers in multivariate datasets. Outlier detection can provide early warning signals for abnormal conditions, allowing experts to identify and address issues before they escalate. However, detecting outliers in…

Learn how to detect outliers using Probability Density Functions and bring fast and explainable results without training labelsPhoto by Randy Fath on UnsplashAnomaly or novelty detection is applicable in a wide range of situations where a clear, early warning of an abnormal condition is required, such as for sensor data, security operations, and fraud detection among others. Due to the nature of the problem, outliers do not present themselves frequently, and due to the lack of labels, it can become difficult to create…

Earth is an outlier — the theoryImage by 0fjd125gk87 from PixabayWhat are outliers?We live on an outlier. Earth is the only hump of rock with life in the Milky Way galaxy. Other planets in our galaxy are inliers or normal data points in a so-called database of stars and planets.There are many definitions of outliers. In simple terms, we define outliers as data points that are significantly different than the majority in a dataset. Outliers are the rare, extreme samples that don't conform or align with the inliers in a…

If you cannot compress it, there is probably something wrong with itPhoto by Randy Fath on UnsplashA classical obstacle you stumble upon in data science and machine learning is outliers. The concept of an outlier is intuitively clear to a human, yet there is no mathematical definition. I think the reason might be that it is pretty darn tricky to come up with one since an outlier can mean different things to different people. Take the following dataset as an example:Image by the author.I claim that most people — including…

Let’s catch those high-dimensional outliersPhoto by João JesusWe’ve all used those simple techniques — plot a scatterplot or a KDE, and the data points farthest from the group are outliers. Now, tell me — how would you use these methods if you were to find outliers in, say, 100-dimensional datasets? Right off the bat, visual outlier detection methods are out of the question.So, fancy machine learning algorithms like Local Outlier Factor or Isolation Forest come to mind, which are effective against outliers in…

Photo by Possessed Photography on UnsplashMethods to replace the necessity of a contamination level in outlier detectionReal life is often chaotic and unpredictable. It seems to like throwing the metaphorical “spanner in the works”, making data usually appear baffling and random. Most data that is recorded or extracted generally requires some form of cleaning before applying further methods like modeling. However, it is often difficult or near impossible to visually distinguish what data is true, noise, or an anomaly.…

True wireless earphones such as the AirPods Pro have demonstrated just how much can be achieved in a small form factor, while the Redmi Earbuds S have proven that you don't need to spend too much for decent true wireless sound. Somewhere between these two extremes is a particularly interesting price segment that doesn't quite get enough attention, due to not having too many high-profile launches. Creative looks to make a mark on the Rs. 5,000 to Rs. 10,000 space with the Outlier Air true wireless earphones.Singapore-based…