Even the most experienced Data Scientists are not always familiar with the best practices involved with developing a Machine Learning pipeline. There is a lot of confusion about what steps should be involved, what should be their sequence and, in general, how to ensure that the insights you create are accurate and valuable. There is also a very limited number of good resources describing a practical and correct approach. However, after many data science projects, you begin to realise the approach to building a pipeline always remains the same. Machine Learning pipelines are modular, and, depending on the situation, some steps can be added or skipped.
Machine learning and data science require more than just throwing data into a python library and utilizing whatever comes out. Data scientists need to actually understand the data and the processes behind the data to be able to implement a successful system. One key methodology to implementation is knowing when a model might benefit from utilizing bootstrapping methods. These are what are called ensemble models. Some examples of ensemble models are AdaBoost and Stochastic Gradient Boosting.
A recent virtual event addressed another such issue: the potential impact machines, imbued with artificial intelligence, may have on the economy and the financial system. The event was organised by the Bank of England, in collaboration with CEPR and the Brevan Howard Centre for Financial Analysis at Imperial College. What follows is a summary of some of the recorded presentations. The full catalogue of videos are available on the Bank of England's website. In his presentation, Stuart Russell (University of California, Berkeley), author of the leading textbook on artificial intelligence (AI), gives a broad historical overview of the field since its emergence in the 1950s, followed by insight into more recent developments.
You've built your machine learning model – so what's next? You need to evaluate it and validate how good (or bad) it is, so you can then decide on whether to implement it. That's where the AUC-ROC curve comes in. The name might be a mouthful, but it is just saying that we are calculating the "Area Under the Curve" (AUC) of "Receiver Characteristic Operator" (ROC). I have been in your shoes.
In continuation of my previous posts on various Performance measures for classifiers, here, I've explained the concept of single score measure namely; 'F - score'. In my previous posts, I had discussed four fundamental numbers, namely, true positive, true negative, false positive and false negative and eight basic ratios, namely, sensitivity(or recall or true positive rate) & specificity (or true negative rate), false positive rate (or type-I error) & false negative rates (or type-II error), positive predicted value (or precision) & negative predicted value, and false discovery rate (or q-value) & false omission rate. I had also discussed accuracy paradox, the relationship between various basic ratios and their trade-off to evaluate the performance of a classifier with examples. I'll be using the same confusion matrix for reference. Precision & Recall: First let's briefly revisit the understanding of'Precision (PPV) & Recall (sensitivity)'.
Would you let a machine learning model that has a failure rate of 98% and a false positive rate of 81% into production? Well, these claimed performance figures are from a facial recognition system that is in use by the policing force in South Wales and other parts of the United Kingdom. Dave Gershgorn article starts with a description akin to the setting of a dystopian future where an overseeing governing system monitors everyone; which is hysterically a foreshadowing of a foreseeable future. South Wales Police have been using facial recognition systems since 2017 and have done this in no secrecy from the public. They've made arrests as a result of the facial recognition system.
In a letter to congress sent on June 8th, IBM's CEO Arvind Krishna made a bold statement regarding the company's policy toward facial recognition. "IBM no longer offers general purpose IBM facial recognition or analysis software," says Krishna. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency." The company has halted all facial recognition development and disapproves or any technology that could lead to racial profiling. The ethics of face recognition technology have been in question for years. However, there has been little to no movement in the enactment of official laws barring the technology.
Detroit's police chief admitted on Monday that facial recognition technology used by the department misidentifies suspects about 96 percent of the time. It's an eye-opening admission given that the Detroit Police Department is facing criticism for arresting a man based on a bogus match from facial recognition software. Last week, the ACLU filed a complaint with the Detroit Police Department on behalf of Robert Williams, a Black man who was wrongfully arrested for stealing five watches worth $3,800 from a luxury retail store. Investigators first identified Williams by doing a facial recognition search with software from a company called DataWorks Plus. Under police questioning, Williams pointed out that the grainy surveillance footage obtained by police didn't actually look like him.
Hawaii is ready for its midpandemic tourism boom. Starting on Aug. 1, tourists looking to visit Hawaii will be able to bypass the state's two-week quarantine requirement for arrivals by getting a negative COVID-19 test within 72 hours before landing in the state. Visitors can also have their quarantines cut short if they receive negative test results during those two weeks. The same rules will also apply to residents returning to the islands. Hawaii won't pay for the tests; travelers will have to handle that themselves before departure, though screeners will still administer temperature checks at airports.
Cancers diagnosed early are often more responsive to treatment. Blood tests that detect molecular markers of cancer have successfully identified individuals already known to have the disease. Lennon et al. conducted an exploratory study that more closely reflects the way in which such blood tests would be used in the future. They evaluated the feasibility and safety of incorporating a multicancer blood test into the routine clinical care of 10,000 women with no history of cancer. Over a 12-month period, the blood test detected 26 cancers of different types. A combination of the blood test and positron emission tomography–computed tomography (PET-CT) imaging led to surgical removal of nine of these cancers. Use of the blood test did not result in a large number of futile follow-up procedures. Science , this issue p. [eabb9601] ### INTRODUCTION The goal of earlier cancer detection is to identify the disease at a stage when it can be effectively treated, thereby offering the patient a better chance of long-term survival. Adherence to screening modalities known to decrease cancer mortality such as colonoscopy, mammography, low-dose computed tomography, and Pap smears varies widely. Moreover, the majority of cancer types are diagnosed only when symptoms occur. Multicancer blood tests offer the exciting possibility of detecting many cancer types at a relatively early stage and in a minimally invasive manner. ### RATIONALE Evaluation of the feasibility and safety of multicancer blood testing requires prospective interventional studies. We designed such a study to answer four critical questions: (i) Can a multicancer blood test detect cancers not previously detected by other means? (ii) Can a positive test result lead to surgical intervention with curative intent? (iii) Can testing be incorporated into routine clinical care and not discourage patients from undergoing recommended screening tests such as mammography? (iv) Can testing be performed safely, without incurring a large number of unnecessary, invasive follow-up tests? ### RESULTS We evaluated a blood test that detects DNA mutations and protein biomarkers of cancer in a prospective, interventional study of 10,006 women who were 65 to 75 years old and who had no prior history of cancer. Positive blood tests were followed by diagnostic positron emission tomography–computed tomography (PET-CT), which served to independently confirm and precisely localize the site and extent of disease if present. The study design incorporated several features to maximize the safety of testing to the participants. Of the 10,006 enrollees, 9911 (99.1%) could be assessed with respect to the four questions posed above. (i) Detection: Of 96 cancers incident during the study period, 26 were first detected by blood testing and 24 additional cancers by conventional screening. Fifteen of the 26 patients in whom cancer was first detected by blood testing underwent PET-CT imaging, and 11 patients developed signs or symptoms of cancer after the blood test that led to imaging procedures other than PET-CT. The specificity and positive predictive value (PPV) of blood testing alone were 98.9% and 19.4%, respectively, and combined with PET-CT, the specificity and PPV increased to 99.6% and 28.3%. The blood test first detected 14 of 45 cancers (31%) in seven organs for which no standard-of-care screening test is available. (ii) Intervention: Of the 26 cancers first detected by blood testing, 17 (65%) had localized or regional disease. Of the 15 participants with positive blood tests as well as positive PET-CT scans, 9 (60%) underwent surgery with curative intent. (iii) Incorporation into clinical care: Blood testing could be combined with conventional screening, leading to detection of more than half of the total incident cancers observed during the study period. Blood testing did not deter participants from undergoing mammography, and surveys revealed that 99% of participants would join a similar, subsequent study if offered. (iv) Safety: 99% of participants did not require any follow-up of blood testing results, and only 0.22% underwent an unnecessary invasive diagnostic procedure as a result of a false-positive blood test. ### CONCLUSION A minimally invasive blood test in combination with PET-CT can safely detect and precisely localize several types of cancers in individuals not previously known to have cancer, in some cases enabling treatment with intent to cure. Further studies will be required to assess the clinical utility, risk-benefit ratio, and cost-effectiveness of such testing. ![Figure] Overview of cancers detected by blood testing. Twenty-six cancers (blue dots) in 10 organs were first detected by blood testing. The blue dots with the red halo represent 12 of the 26 cancers that were surgically treated with intent to cure. Nine of these 12 were detected by the combination of the blood test and PET-CT, with the remaining three identified by the blood test combined with another imaging modality. Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography–computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure. : /lookup/doi/10.1126/science.abb9601 : pending:yes