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Articles related to "score"


Court OKs Barring High IQs for Cops (2000)

  • 8, 2000 -- A man whose bid to become a police officer was rejected after he scored too high on an intelligence test has lost an appeal in his federal lawsuit against the city.
  • The 2nd U.S. Circuit Court of Appeals in New York upheld a lower court’s decision that the city did not discriminate against Robert Jordan because the same standards were applied to everyone who took the test.
  • Jordan, a 49-year-old college graduate, took the exam in 1996 and scored 33 points, the equivalent of an IQ of 125.
  • But the U.S. District Court found that New London had “shown a rational basis for the policy.” In a ruling dated Aug. 23, the 2nd Circuit agreed.
  • The court said the policy might be unwise but was a rational way to reduce job turnover.
  • Jordan has worked as a prison guard since he took the test.

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Simple Text Summarizer Using Extractive Method

  • Have you seen applications like inshorts that converts the articles or news into 60 words summary.
  • In this article, we will build a text summarizer with extracted method that is super easy to build and very reliable when it comes to results.
  • The 4th line is used to install the nltk(natural language toolkit) package that is the most important package for this tutorial.
  • Here, we have simply used the sent_tokenizefunction of nltk to make the list that contains sentences of the article at each index.
  • In the screenshot, you can see the dictionary containing every word with its count in the article(higher the frequency of the word, more important it is).
  • Then simply joined the list of selected sentences to form a single string of summary.
  • The final output summary for the Natural Language Processing article can be seen in the screenshot attached.

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Simple Text Summarizer Using Extractive Method

  • Have you seen applications like inshorts that converts the articles or news into 60 words summary.
  • In this article, we will build a text summarizer with extracted method that is super easy to build and very reliable when it comes to results.
  • The 4th line is used to install the nltk(natural language toolkit) package that is the most important package for this tutorial.
  • Here, we have simply used the sent_tokenizefunction of nltk to make the list that contains sentences of the article at each index.
  • In the screenshot, you can see the dictionary containing every word with its count in the article(higher the frequency of the word, more important it is).
  • Then simply joined the list of selected sentences to form a single string of summary.
  • The final output summary for the Natural Language Processing article can be seen in the screenshot attached.

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Building the “Hello World” of Kaggle projects using AutoAI

  • Kaggle provides data science enthusiasts a platform for analytics competitions in which companies and researchers post data to allow enthusiasts to compete to produce the best models for predicting and describing the data.
  • Simply create an AutoAI project in Watson Studio, give your experiment a name, and upload your train.csv file.
  • AutoAI takes the dataset and the target variable to design pipelines (these are different models) using various HPO (hyperparameter optimization) parameters and enhanced feature engineering for each pipeline to get the best model.
  • As you might already know, there are different ways to evaluate and select the best model such as Accuracy, F1 score, precision, etc.
  • Now that we have our model, let's create a python script to batch score the AutoAI model against our test.csv to submit our results.

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Building the “Hello World” of Kaggle projects using AutoAI

  • Kaggle provides data science enthusiasts a platform for analytics competitions in which companies and researchers post data to allow enthusiasts to compete to produce the best models for predicting and describing the data.
  • Simply create an AutoAI project in Watson Studio, give your experiment a name, and upload your train.csv file.
  • AutoAI takes the dataset and the target variable to design pipelines (these are different models) using various HPO (hyperparameter optimization) parameters and enhanced feature engineering for each pipeline to get the best model.
  • As you might already know, there are different ways to evaluate and select the best model such as Accuracy, F1 score, precision, etc.
  • Now that we have our model, let's create a python script to batch score the AutoAI model against our test.csv to submit our results.

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TechEmpower Framework Benchmarks Round 19

  • This project measures the high-water mark performance of server side web application frameworks and platforms using predominantly community-contributed test implementations.
  • Round 19 introduces two new features in the results web site: Composite scores and a hardware environment score we're calling the TechEmpower Performance Rating (TPR).
  • With the composite scores described above, we are now able to use web application frameworks to measure the performance of hardware environments.
  • We believe this could be an interesting measure of hardware environment performance because it's a holistic test of compute and network capacity, and based on a wide spectrum of software platforms and frameworks used in the creation of real-world applications.
  • Hardware performance measurements must use the specific commit for a round (such as 801ee924 for Round 19) to be comparable, since the test implementations continue to evolve over time.

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Truthset raises $4.75M to help marketers score their data

  • More specifically, the company scores the consumer data that marketers are buying on accuracy, on a scale between 0.00 and 1.00.
  • To create these scores, Truth{set} checks the data against independent data sources, as well as first-party data and panels.
  • In addition to coming out of stealth,  Truth{set} is also announcing that it has raised $4.75 million in seed funding from startup studio super{set}, WTI, Ulu Ventures, and strategic angel investors.
  • Throughout our conversation, he emphasized the idea of independence, arguing that in order to provide trustworthy scores, “You cannot have a conflict of interest.” At the same time, Truthset is working closely with the data providers to score their data and to help them improve their accuracy.
  • The goal is to create an expectation among marketers that if data is accurate, it will come with a score from Truth{set}.

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How Objective are Sports Articles?

  • In this tutorial, we will be looking at the Sports Article Dataset and building beautiful word clouds out of those articles, as well as analyzing the independent features within these 1000 articles to figure out their objectivity/subjectivity score.
  • This tutorial and implementation is part of my random dataset challenge where I build different Machine Learning models to expand my data science skills.
  • Let’s start off this time by building a Word Cloud, actually, there’s 2 of them, so we can take a look at the most frequently appearing words.
  • As can be seen, our model correctly predicted (predicted = actual), 168 objective articles and 80 subjective articles.
  • Integrating both the text articles and the quantifiable features dataset, I thought of drawing a Word Cloud because I’d always wanted to know how to do it when I came across it in several other articles.

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Popular COVID-19 videos on YouTube misinform the public

  • False or misleading information in some of YouTube’s most popular COVID-19 videos has had more than 62 million views.
  • A study that BMJ Global Health recently published has found that 1 in 4 of the most viewed YouTube videos discussing SARS-CoV-2 contain misleading or inaccurate information.
  • While plenty of good information about the novel coronavirus is available on YouTube, nonfactual or misleading videos seem to be just as appealing to online audiences.
  • Stay informed with live updates on the current COVID-19 outbreak and visit our coronavirus hub for more advice on prevention and treatment.
  • For each video, the team awarded a CSS point for the presence of exclusively factual information regarding how the virus spreads, how to prevent it from spreading, typical symptoms, possible treatments, and the epidemiology of the disease.
  • On the positive side, almost three-quarters of the videos that the team collected contained only accurate, factual information.

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Sentiment Analysis: VADER or TextBlob?

  • To outline the process very simply:1) Tokenize the input into its component sentences or words.2) Identify and tag each token with a part-of-speech component (i.e., noun, verb, determiners, sentence subject, etc).3) Assign a sentiment score from -1 to 1.4) Return score and optional scores such as compound score, subjectivity, etc.
  • From the above, we can see the IMDB statement is deemed as negative, but not heavily so, and the Twitter statement is very positive.The subjectivity is TextBlobs score of whether the statement is deemed as more opinion, or fact based.
  • VADER operates on a slightly different note, and will output scoring in 3 classifications levels, as well as a compound score.From the above, we can see the IMDB review has ~66% of the words falling into a neutral category of sentiment, however its compound score — which is a “normalized, weighted, composite score” flags it as a very negative statement.The Twitter statement again comes up as very positive based on its 0.9798 compound score.

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