Examples of using Machine learning model in English and their translations into Hungarian
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Colloquial
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Official
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Medicine
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Ecclesiastic
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Financial
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Programming
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Official/political
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Computer
We built a machine learning model to answer those questions.
(2010) used the Galaxy Zoo classifications to train a machine learning model to do galaxy classification.
Next, use that machine learning model to impute the survey answers of everyone in the digital trace data.
Then, for a subset of the images,the Galaxy Zoo labels are used to train a machine learning model.
(2010) built a machine learning model that could predict the human classification of a galaxy based on the characteristics of the image.
Then, for a subset of the images,the Galaxy Zoo labels are used to train a machine learning model.
First, for the people in both data sources, build a machine learning model that uses the big data source to predict survey answers.
We have a machine learning model that essentially learns against Google search results and builds the best possible model it can.
(2010) used the Galaxy Zoo classifications to train a machine learning model to do galaxy classification.
She built her first machine learning model with Azure ML Studio after seeing a demo from a Cloud Developer Advocate and being inspired to learn more.
There are two steps. First, for the people in both data sources, build a machine learning model that uses digital trace data to predict survey answers.
If this machine learning model could reproduce the human classifications with high accuracy, then it could be used by Galaxy Zoo researchers to classify an essentially infinite number of galaxies.
First, for the people in both data sources, build a machine learning model that uses digital trace data to predict survey answers.
Researchers interested in creating what I have called second generation human computation systems(e.g.,systems that use human labels to train a machine learning model) might be interested in Shamir et al.
Then, they used this hand-labeled data to create a machine learning model that could infer the sentiment of a post based on its characteristics.
Researchers interested in creating what I have called computer-assisted human computation systems(e.g.,systems that use human labels to train a machine learning model) might be interested in Shamir et al.
Boston Consulting Grouphas reported an oil refinery machine learning model that uses 1,000 data points to better predict equipment failures.
(2010) machine learning model were more complex than those in my toy example- for example, she used features like“de Vaucouleurs fit axial ratio”- and her model was not logistic regression, it was an artificial neural network.
Then, King and colleagues used this hand-labeled data to estimate a machine learning model that could infer the sentiment of a post based on its characteristics.
Building a machine learning model that can correctly reproduce the human classifications is itself a hard problem, but fortunately there are already excellent books dedicated to this topic(Hastie, Tibshirani, and Friedman 2009; Murphy 2012; James et al. 2013).
Combining these two sources of data,they used the survey data to train a machine learning model to predict a person's wealth based on their call records.
More specifically, they used their machine learning model, which was trained on their sample of about 1,000 people, to predict the wealth of all 1.5 million people in the call records.
Given the data matrix and the desired output(e.g., whether the image was classified by a human as an elliptical galaxy),the researcher creates a statistical or machine learning model- for example, logistic regression- that predicts the human classification based on the features of the image.
These data will be use to train a machine learning model that will be applied to an in silico library of over a billion molecules to search for potential novel antiviral compounds.
Further, foreshadowing a theme that will occur throughout this book, the supervised learning approach that they used-hand-labeling some outcomes and then building a machine learning model to label the rest- turns out to be very common in social research in the digital age.
Given this possibility,Blumenstock asked whether it was possible to train a machine learning model to predict how someone will respond to a survey based on their call records.
More specifically, using the human classifications created by Galaxy Zoo,Banerji built a machine learning model that could predict the human classification of a galaxy based on the characteristics of the image.