Prominent tech executives, including Tesla ceo elon Musk and Apple ceo tim cook, have criticized team Zuck. Musk, who deleted Tesla and SpaceX pages from Facebook, said the social network gave him "the willies." cook said Facebook failed to regulate itself and vowed Apple wouldn't make money off its user's data. "It's clear now that we didn't do enough to prevent these tools from being used for harm zuckerberg said during his comments during two-days of testimony to congress last month. "That goes for fake news, foreign interference in elections and hate speech, as well as developers and data privacy. We didn't take a broad enough view of our responsibility, and that was a big mistake." In Europe, zuckerberg has to contend with regulators who take a much stronger stance on privacy than in the. Among the questions Verhofstadt asked - and didn't get an answer to - was one about the general Data Protection Regulation, or gdpr.
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And now they're going to have to wait for answers. Rebuilding trust, so far, zuckerberg's added new privacy controls that let people clear their web and bibliography app histories from Facebook, and he's promised that the 10,000 curators the company is hiring this year will clean up fake news, hate speech and other objectionable content found. Facebook's chief also told eu lawmakers tuesday that the company will add 3,000 workers across 12 European cities this year to help in its fight against online abuse, hate speech and election interference. Now Playing: Watch this: Zuck's mea culpa to eu parliament 3:20, zuckerberg has said the hiring is needed to address concerns that bad actors in Russia had used Facebook to spread propaganda and misinformation during the 2016 us presidential election. When he introduced a new dating feature for Facebook at the company's annual F8 developer conference last month, he was quick to add that it had been designed with " privacy and safety in mind from the beginning. still, that hasn't been enough. Some advertisers, including Firefox web browser maker mozilla and speaker maker Sonos, stopped advertising on Facebook as the scandal was unfolding. And while users started a campaign called DeleteFacebook, the company said it actually saw user growth during the three months ended March. And through it all, it turns out Facebook still pulled in money hand over fist - counting nearly 5 billion in profits during that same time, a 63 percent increase over the previous year - by using the details its users share to direct more. Facebook makes the majority of its money selling ads.
3:17, zuckerberg insisted that hate speech, terror and violence have "no place on our services." he added that his team is is creating artificial intelligence tools to identify, for example, almost all the british content from isis. He also said Facebook is getting better at identifying bullying and possibilities of self harm. "We'll never be perfect zuckerberg said. "Our adversaries, especially on the election side - the people trying to interfere - will have access to the same ai tools that we will. So it's an arms race, and we'll constantly be working to stay ahead.". Zuckerberg ended the session telling Parliament, "I want to be sensitive to time because we are 15 minutes over.". But members of Parliament didn't care about time limits.
And others raised concerns about free speech allowing for nazi propaganda. Verhofstadt suggested that Facebook may be running afoul of European antimonopoly laws, especially because facebook's Messenger and WhatsApp are among the outsiders most popular messaging services in the world. He asked if Facebook will open its books to european regulators to consider whether his company is a monopoly. "It's not enough to say 'we're going to fix it ourselves. Meanwhile, nigel Farage, who heads up Europe of Freedom and Direct Democracy, the european Parliament's right-wing populist group, asked Zuckerberg to defend the platform's political leanings and its transparency. Right-leaning Facebook users who hold mainstream, not extremist, political views "are being willfully discriminated against he said. "Would you accept that today facebook is not a platform for all ideas that is operated impartially?" said Farage. "I'm not someone who calls for legislation on the international stage, but I'm starting to think that we need a social media bill of rights. Now Playing: Watch this: eu parliament member to zuck: Did you create a digital.
Getty Images, the 34-year-old multibillionaire has been answering questions for weeks about everything from Russian meddling in the 2016 us presidential election, which some argue handed a victory to donald Trump, to the 87 million user profiles that were mistakenly shared with a now-defunct uk-based. European regulators were clearly unhappy. "This represents an attack on our fundamental values said. European Parliament President Antonio tajani on tuesday. "We need to prevent this from happening again.". One noted that Facebook had learned about Cambridge Analytica three years ago, but only acknowledged recently that the firm had gotten access to users' data. Another pointed to the pervasiveness of Facebook's data collection.
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Speed, this refers to the computational cost in generating and using the classifier or predictor. Robustness, it refers to the ability of classifier or predictor to make correct predictions from given noisy data. Scalability scalability refers to the ability to construct the classifier or predictor efficiently; given large amount of data. Interpretability it refers to what extent the classifier or predictor understands. Previous Page Print Next Page. Mark zuckerberg went to Brussels on the latest stop of his apology tour tuesday to deliver yet another mea culpa for privacy and policy blunders that led to one of the largest data favorite leaks. Facebook's history and an unprecedented attack on democratic elections across the west.
If this was supposed to be part of a charm offensive for Facebook, it fell flat. After listening to about an hour of questions from members of the european Union's Parliament, zuckerberg answered at the end - rather than responding to each question after it was posed. But he ended up only spending about 25 minutes giving his replies, ignoring some questions completely. "I asked you six yes-and-no questions, and I got not a single answer said guy verhofstadt, a belgian politician. Zuckerberg paused and then responded, "I'll make sure we follow up and get you answers to those" in the next few days. Cardboard cutouts of Mark zuckerberg with the words "Fix fakebook" on their chests were staged in front of the eu's hearing tuesday.
Data Transformation and reduction, the data can be transformed by any of the following methods. The data is transformed using normalization. Normalization involves scaling all values for given attribute in order to make them fall within a small specified range. Normalization is used when in the learning step, the neural networks or the methods involving measurements are used. Generalization, the data can also be transformed by generalizing it to the higher concept.
For this purpose we can use the concept hierarchies. Note, data can also be reduced by some other methods such as wavelet transformation, binning, histogram analysis, and clustering. Comparison of Classification and Prediction Methods. Here is the criteria for comparing the methods of Classification and Prediction. Accuracy, accuracy of classifier refers to the ability of classifier. It predict the class label correctly and the accuracy of the predictor refers to how well a given predictor can guess the value of predicted attribute for a new data.
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Here the test data is used to estimate the accuracy of classification rules. The classification rules can be applied to the new data tuples if the accuracy is considered acceptable. Classification and Prediction Issues, good the major issue is preparing the data for Classification and Prediction. Preparing the data involves the following activities. Data Cleaning, data cleaning involves removing the noise and treatment of missing values. The noise is removed by applying smoothing techniques and the problem of missing values is solved by replacing a missing value with most commonly occurring value for that attribute. Relevance Analysis, database may also have the irrelevant attributes. Correlation analysis is used to know whether any two given attributes are related.
Building the Classifier or Model, using Classifier for Classification, building the Classifier or Model. This step is the learning step or the learning phase. In this step the classification algorithms build the classifier. The classifier is built toys from the training set made up of database tuples and their associated class labels. Each tuple that constitutes the training set is referred to as a category or class. These tuples can also be referred to as sample, object or data points. Using Classifier for Classification, in this step, the classifier is used for classification.
predict a numeric value. Therefore the data analysis task is an example of numeric prediction. In this case, a model or a predictor will be constructed that predicts a continuous-valued-function or ordered value. Note, regression analysis is a statistical methodology that is most often used for numeric prediction. How does Classification Works? With the help of the bank loan application that we have discussed above, let us understand the working of classification. The data Classification process includes two steps.
Classification, prediction, classification models predict categorical class labels; and prediction models predict continuous valued functions. For example, we can build a classification model to categorize bank loan applications as either safe or risky, or a prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation. Following are the examples of cases where the data analysis task is Classification. A bank loan officer wants to analyze the data in order to know which customer (loan applicant) are risky or which are safe. A marketing short manager at a company needs to analyze a customer with a given profile, who will buy a new computer. In both of the above examples, a model or classifier is constructed to predict the categorical labels. These labels are risky or safe for loan application data and yes or no for marketing data.
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Previous Page, next Page, data mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data. The tutorial starts off with a basic overview and the terminologies involved in data mining and then gradually moves on to cover topics such as knowledge discovery, query language, classification and prediction, decision tree induction, cluster analysis, and how to mine the web. This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining. Before proceeding with this tutorial, you should have an understanding of the basic database concepts such as schema, er model, Structured query language and a basic knowledge of Data warehousing concepts. Advertisements, previous Page, next Page, there are writing two forms of data analysis that can be used for extracting models describing important classes or to predict future data trends. These two forms are as follows.