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Shapash- Python Library To Make Machine Learning Interpretable


The above quote is quite interesting and yes, they speak the truth most of us are from the technical field so we probably know about what machine learning is? it is the current worldwide digital technology ruled over the world. If you are familiar with machine learning then you come across the words data, train, test, accuracy, and many more, and many of you are capable of writing machine learning scripts if you notice that we didn't see the background calculations of the machine learning models because machine learning is not interpretable. Many people say that the machine learning models are the black box models, suppose if we give input there are a lot of calculations are happening inside and we got the output, that particular calculation based on what feature we are actually giving. Suppose we give the input of 5 features inside this, it may be a situation where some of the feature value may be increasing and some of them are decreasing, so we not able to see this, but python has a beautiful library which makes a machine learning model interpretable by this we can able to understand that underground calculations. This beautiful library is developed by a group of MAIF Data Scientists.

Top Stories, Mar 29 – Apr 4: Top 10 Python Libraries Data Scientists should know in 2021; Shapash: Making Machine Learning Models Understandable - KDnuggets


Shapash: Making Machine Learning Models Understandable, by Yann Golhen What's ETL?, by Omer Mahmood Easy AutoML in Python, by Dylan Sherry Deep Learning Is Becoming Overused, by Michael Grogan The 8 Most Common Data Scientists, by JABDE How To Overcome The Fear of Math and Learn Math For Data Science, by Arnuld On Data More Data Science Cheatsheets, by Matthew Mayo How to Succeed in Becoming a Freelance Data Scientist, by Devin Partida Top 10 Python Libraries Data Scientists should know in 2021, by Terence Shin Are You Still Using Pandas to Process Big Data in 2021? Are You Still Using Pandas to Process Big Data in 2021?

SoftBank to tighten governance standards for firms it invests in: report

The Japan Times

NEW YORK – Following related issues at WeWork, SoftBank Group Corp. will introduce new standards to tighten corporate governance at companies in which it invests, the Financial Times reported Monday. SoftBank is expected to outline the new standards Wednesday, the British newspaper said, citing people briefed on the plan. The tougher governance standards will apply to future investments made by SoftBank. Meanwhile, its Saudi Arabia-backed Vision Fund is in discussions about how it can adopt some or all of these measures, according to the paper. For private companies, SoftBank will look to have at least one board seat, require at least one independent director and prohibit directors from owning "supervoting" shares, the report said.

AI governance: Reducing risk while reaping rewards


As a result, addressing governance of the use of artificial intelligence technologies requires action on many levels. "It does not start at the IT level or the project level," says Kamlesh Mhashilkar, head of the data and analytics practice at Tata Consultancy Services. AI governance also happens at the government level, at the board of directors level, and at the CSO level, he says. Get the latest insights with our CIO Daily newsletter. In healthcare, for example, AI models must pass stringent audits and inspections, he says.

Data-driven innovation needs trustworthy governance


Now is the time to take stock, consider how we can maintain and improve upon the data-driven innovation we have seen, and build the governance that is worthy of the trust of citizens over the long term. Trust can be fragile and fleeting, but I know from my experience as an entrepreneur working in the data and technology industry that action can be taken to build and maintain it. For example, the adoption of consistent standards and clear governance frameworks can enable organisations to adopt new technologies with confidence, and can help organisations to earn the trust of citizens to provide their data. This is a route to use data for the benefit of businesses and citizens alike. The CDEI's research shows that public support for greater use of digital technology is closely related to trust in its governance.