Sentences with phrase «scoring algorithms used»

You need at least one revolving account to meet the minimum criteria of the scoring algorithms used by all three credit bureaus.

«Zap's central component is a scoring algorithm used to gauge the readiness of a consumer to buy,» Yannaccone continues.

Not exact matches

Our algorithms look at 450 popcorn companies across the country and score them on metrics around brand engagement — how often and quickly consumers talk about the brands, the sentiment, the word choice people use.

«Google uses its Quality Score algorithm to rate the quality and relevance of your keywords and AdWords ads,» SEO expert Larry Kim points out.

The resulting dataset was then used to train a classifier algorithm that gives any headline posted on Facebook a «clickbait» score based on patterns.

The company uses the algorithm, which it says is validated and 94 % accurate, to assign risk scores to patients and target them with varying modes of outreach — Henry says those efforts are «soft touch, nothing Orwellian.»

Prattle uses a machine - learning algorithm to give each Fed communication a score, with a positive score providing a hawkish sentiment, and a negative score a dovish sentiment.

So, depending on the version of the FICO algorithm that the agency uses, your score might differ, since the versions take each factor that goes into your score into account slightly differently to come back with your number.

If you're paying your bills on time, utilizing not too much of your credit limit, and only opening new credit accounts when you need to, you'll be able to maintain a good score — no matter which bureau is reporting it and no matter which version of the algorithm they use.

The project is detailed in the contract as a seven step process — with Kogan's company, GSR, generating an initial seed sample (though it does not specify how large this is here) using «online panels»; analyzing this seed training data using its own «psychometric inventories» to try to determine personality categories; the next step is Kogan's personality quiz app being deployed on Facebook to gather the full dataset from respondents and also to scrape a subset of data from their Facebook friends (here it notes: «upon consent of the respondent, the GS Technology scrapes and retains the respondent's Facebook profile and a quantity of data on that respondent's Facebook friends»); step 4 involves the psychometric data from the seed sample, plus the Facebook profile data and friend data all being run through proprietary modeling algorithms — which the contract specifies are based on using Facebook likes to predict personality scores, with the stated aim of predicting the «psychological, dispositional and / or attitudinal facets of each Facebook record»; this then generates a series of scores per Facebook profile; step 6 is to match these psychometrically scored profiles with voter record data held by SCL — with the goal of matching (and thus scoring) at least 2M voter records for targeting voters across the 11 states; the final step is for matched records to be returned to SCL, which would then be in a position to craft messages to voters based on their modeled psychometric scores.

The contract stipulates that all monies transferred to GSR will be used for obtaining and processing the data for the project — «to further develop, add to, refine and supplement GS psychometric scoring algorithms, databases and scores» — and none of the money paid Kogan should be spent on other business purposes, such as salaries or office space «unless otherwise approved by SCL».

No doubt, it is a dismaying picture that confronts us: British company SCL Group, operating under the brand name Cambridge Analytica with the supervision of Steve Bannon, obtained data collected from Facebook by Cambridge University academic Alexandr Kogan, and used systems built by data scientist and whistleblower - to - be Chris Wylie to train its microtargeting algorithms to nudge scores of already - angry voters towards electing Donald Trump and leaving the European Union — a set of experiments largely bankrolled by US hedge - fund billionaire Robert Mercer, 90 % owner of Cambridge Analytica.

There is a complex algorithm used to calculate your credit score.

Using proprietary algorithms, the Sentiment Score shows what percentage of tweets are positive and displays other relevant metrics.

The algorithm which uses your credit report to determine your credit score is cloaked; we don't know how each line item affects the final score.

FICO has created the algorithm — of the same name — that most lenders in the United States use to find your credit score when you apply for a loan.

The NPSC score is calculated with the use of the NPSC algorithm by allocating the following points: baseline points for amounts of risk - associated nutrients in a food (energy, saturated fat, total sugars, and sodium); points that are based on the contents of fruit, vegetables, nuts, and legumes; points that are allocated to a food on the basis of its protein content; and, in the case of category 2 or 3 foods, points that are allocated to a food on the basis of its fiber content.

A very, very complicated algorithm was used to compute these scores, and they definitely weren't pulled from an unspeakable place after a split - second of thought.

Our compatibility survey uses a proprietary algorithm to determine your compatibility with other prospective parenting partners along these lines as well as other inputs from your profile, and calculates a «Compatibility Score» based on your inputs and the inputs of the other prospective parenting partner.

No doubt, it is a dismaying picture that confronts us: British company SCL Group, operating under the brand name Cambridge Analytica with the supervision of Steve Bannon, obtained data collected from Facebook by Cambridge University academic Alexandr Kogan, and used systems built by data scientist and whistleblower - to - be Chris Wylie to train its microtargeting algorithms to nudge scores of already - angry voters towards electing Donald Trump and leaving the European Union — a set of experiments largely bankrolled by US hedge - fund billionaire Robert Mercer, 90 % owner of Cambridge Analytica.

But Mattingly argues that his algorithms are more transparent and so can be used to calculate a score that judges might prefer.

It used to be tied to «likes» and clicks, but after extensive research on how to capture people's deeper interests, the algorithm has been tweaked to rank content by a «relevance score.»

In patients with chronic cerebrovascular disease and comorbidities, a shortened telomere G - tail length was associated with age and Framingham risk score, which is an algorithm used to estimate the 10 - year cardiovascular risk of an individual.

These changes, which were still present two years after birth, predicted women's scores on a test of maternal attachment, and were so clear that a computer algorithm could use them to identify which women had been pregnant.

They used three validated biomarkers TNFR1, ST2 and Reg3α to create an algorithm that calculated the probability of non-relapse mortality (usually caused by GVHD) that provided three distinct risk scores to predict the patient's response to GVHD treatment.

These ratings were then used to train a machine - learning algorithm to extract a single score from the measured values that would faithfully reflect the perceptual judgement of the volunteers.

Using data from 58 of the 59 infants, the algorithm picked out the brain connections that differ between children with and without autism, and that track with scores on any of the behavioral tests.

In order to know whether the algorithm has provided the computer with an accurate representation of a word it compares similarity scores produced using the word representations learnt by the computer algorithm against human rated similarities.

The error rate of all algorithms is greatly reduced by using statistical scores to evaluate matches rather than percentage identity or raw scores.

Using an advanced algorithm, Fitcode then pulls together a personalized boutique for you, showing only designer jeans that align with your Fitcode score.

Risk is identified for consumers using a 26 - metric algorithm which generates a relevance scores for each community.

If not, we use algorithms to identify comparison students, employing a standard approach to matching on prior test scores and achievement.

Using data on test scores and student records from the Chicago Public Schools, we developed a statistical algorithm to identify classrooms where cheating was suspected.

Scores are not rolled up into a normalizing algorithm which in the case of a high - stakes assessment might restrict scores with accommodations from beingScores are not rolled up into a normalizing algorithm which in the case of a high - stakes assessment might restrict scores with accommodations from beingscores with accommodations from being used.

The American Educational Research Association became the latest organization to caution against using value - added models — complex algorithms that attempt to measure a teacher's impact on student test scores — to evaluate teachers and principals.

Student responses can be objectively scored using artificial intelligence and computer algorithms to minimize unwanted variance in student scores;

Chicago - based TeacherMatch, which says it uses algorithms to predict a teacher candidate's effect on student test scores, sounds like something «straight off the cover of the Onion,» Vieth writes on her blog «Running Reflections.»

One teacher asked for more details about a complex algorithm the state will use to measure a teacher's effect on student test score growth known as value - added measurement.

She says the algorithms and cut scores used to rate teachers were arbitrary.

A Chicago based company, TeacherMatch, claims to use algorithms to predict the effect that a teacher candidate will have on value added student test scores.

The quality score means that an algorithm is used to sample adjacent pixels to decide what color to show in any given pixel.

Specifically, FICO — the data analytics company whose algorithms generate credit scores — can not generate a score unless you have at least one account you've used over the previous six months.

Using a proprietary risk model, LendingPoint combines hundreds of data points with algorithms to get a more complete financial story, often leading to approving those who might otherwise have been declined based on their credit score alone.

Different credit cards use different algorithms to calculate scores.

Student loans are included in one out of two different debt utilization ratios used by credit scoring algorithms.

Credit scores are factored by Credit Monitoring Services by using a complex algorithm.

That's because each of the three major credit bureaus (Equifax, Experian and TransUnion) use the FICO algorithm to produce a score based on its unique data set.

They each use their own model that is based on the FICO model but they apply their own algorithm to generate a score.

A complex algorithm is then used to determine your unique credit score, which is updated on a monthly basis.

FICO, the company that developed the original algorithm credit agencies use to calculate credit scores, recently made an announcement regarding certain collection accounts.