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Sorting algorithms are to Machine Learning what the sorting hat is to students in the Harry Potter series: a way to assign each individual to a class (or a house). In Harry Potter, there were four different houses: Gryffindor, Slytherin, Ravenclaw, and Hufflepuff. The magical sorting hat assigns every first-year student into one of the four houses. The sorting hat ‘classifies’ the students into four different categories.

We have previously discussed binary classification. When the number of classes exceeds two, the terminology used is ‘multiclass’ classification.

We explained the various metrics used in binary classification, namely: the confusion matrix, Accuracy, Recall, Precision, MCC, F1 score. All these metrics apply to multiclass classification. …


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Can Machine Learning predict the size of a person based on his/her age and weight? Can Machine Learning compute the price of a house based on its surface, location, and number of rooms? Can Machine Learning determine the revenue of a company based on its marketing budget? These are all typical examples of regression problems.

Metrics for Regression Algorithms

Last time we wrote about binary classification and the corresponding Machine Learning Metrics. We explained that Binary Classification consists of assigning an individual to one of two classes. An illustration is a medical diagnosis for COVID-19 infection. A person is either infected or not.

Regression, in contrast to binary classification, provides an estimate of a continuous value. For instance, what is the forecasted height of a male subject aged 52 who weighs 80 kilos? …


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Artificial Intelligence applied to COVID, i.e., COVID AI, is key to fighting the deadly virus. Over the past ten months, the virus has taken more lives than H.I.V., malaria, influenza, and cholera over the same period. And the COVID-19 pandemic continues to propagate around the globe.

Fighting deaths: entering Clinical Trials

Since outbreaks started at the end of 2019, the coronavirus has ripped through country after country. According to Johns Hopkins University data, it has been sickening more than 34 million people worldwide and caused over 1.13 million mortality. A severe toll gathered from official counts, yet one that notably underestimates how many have really perished. …


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The Machine Learning challenge: “Measure what is measurable, and make measurable what is not.”

This quote from Galileo applies perfectly to Machine Learning Algorithms. How can we make the difference between a well-performing Machine Learning Algorithm and a poor-performing one? Is there a metric used as a magical wand to separate the wheat from the chaff?

Metrics are amazingly powerful. They are figures and statistics. As such, they have a tremendous influence. They appear as “truths.”

When, in substance, their real power is to tell a story. Their importance is demonstrated by how much damage they can do. …


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We hear a lot about intubation lately when discussing patients with COVID-19-related respiratory problems. Yet the procedure isn’t particular to COVID. Some people are too vulnerable to breathe autonomously. For instance, they have chronic obstructive pulmonary disease (COPD) or pneumonia.

How does intubation work?

The medical staff intubates a patient by placing an endotracheal tube into a person’s trachea through the mouth or nose. It helps open up the patient’s airways to secure breathing. It’s simple and straightforward airway management to prevent obstruction. …


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According to the Center for Disease Control, the prevalence of Coronavirus infections is more than ten times higher than the official number of cases in six regions of the United States. The meaning of prevalence here is ‘the population share affected by a medical condition such as a coronavirus at a specific time.’ Coronavirus predictions often involve prevalence estimations. Because, when the prevalence reaches 60%, herd immunity is achieved, or so we hope. Assuming immunity lasts and prevalence is well calculated. Recent research studies from Spain and England show that immunity fades away.

Herd immunity and silent infection: friends or foes?

There have been more than 10.7 million diagnosed cases worldwide, including over 516 thousand deaths since October 2019. The figures published by the Center for Disease Control (C.D.C) suggest that these figures might be well off the real count of people infected. …


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On June 9, 2020, the number of cases with coronavirus disease in France hospitalized in intensive care dropped under the 1,000 threshold for the first time since the start of the outbreak. While on July 10, 2020, this same metric entered the red zone in Florida. As many as 48 hospitals in Florida have reached their full capability in their intensive care units and present zero ICU beds, reported CNN. Another 52 Florida hospitals have less than 10 percent ICU beds available, according to data released by the Agency for Health Care Administration (AHCA). During this period, the sunshine state announced almost 9,000 new coronavirus disease cases. And at least 120 coronavirus disease-related deaths, making the cumulative cases more than 232,000 with over 4,000 coronavirus-related deaths in the state, said the report.


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Among the many lingering issues surrounding the spread of coronavirus and its long-term development is “How to make a correct diagnosis of the disease?”.

Are 80% of COVID-19 patients asymptomatic? The prevalence is the contamination rate among the population. What is the prevalence of SARS-CoV-2 antibody in the U.S.? In Europe? The prevalence expectedly varies from less than 5% to 25% in the U.S. and most European countries. The prevalence percentages stand between 10 and 15% in Southern Europe: 13% in Italy, 11% in Spain, 15% in France. But with significant regional differences, in France, the Grand Est (North East) is estimated to have a prevalence of over 20% while the South West is under 5%. In the state of New York (USA), the prevalence rate is 13.9%, with a high 21.2% in New York City. But, both in L.A. County and in Santa Clara, it is between 4 and 9%. …


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Does Data Analytics Matter?

In these early days of data capture and analysis, “data analytics” can mean a huge number of things, ranging from simple presentation graphs to sophisticated prediction apparatus. From a practical sense, “data analytics” perhaps is best defined as “the ability to derive ROI from information”.

How to quantify ROI from information?

The ROI obtained from information can be described in much the same way as ROI from any other investment of resource: in terms of cost and return.

Costs can be measured in traditional ways:

  • expenditure of capital,
  • investment of intellectual property,
  • investment of time,
  • investment of other finite resources.

Ultimately, ROI can be measured in traditional ways as well: derived value from the final product. Value may vary from use case to use case. In many business use cases, value is incremental revenue produced as a result of the activity. In others, it may be the attainment of an objective, such as solving a specific problem. In all cases, it can ultimately be reduced down to the traditional measure used to assess ROI in non-analytical activities. …


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Artificial Intelligence (AI) is among the most discussed topics of the last 10 years. As the volume of collected data increases dramatically, technics such as Machine Learning, Deep Learning or Predictive Modeling become a part of our everyday concerns.

Let’s take a look at Predictive Modeling. It can be defined as the process that uses a historical dataset to build a mathematical solution with the purpose to predict outcomes from new data. Basically, it means that you use old data with verified results to build a mathematical model. This model will then be used to calculate predictions, i.e. …

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The Small Data Predictive Modeling Company. Self-service AI for Small Data : www.mydatamodels.com

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