Examples of using Learning model in English and their translations into Urdu
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Higher Expectations Learning Model.
Next, use that machine learning model to impute the survey answers of everyone in the digital trace data.
Then, for asubset of the images, the Galaxy Zoo labels are used to train a machine learning model.
Third, the researchers trained a supervised learning model to classify the sentiment of posts.
The AI machine learning models used in the new study are known as'random forest' and'deep learning'.
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(2010) used the Galaxy Zoo classifications to train a machine learning model to do galaxy classification.
(2010) built a machine learning model that could predict the human classification of a galaxy based on the characteristics of the image.
Our Elk Grove Resource Center offers ahybrid 2 day/week independent study learning model for grades 1st- 12th.
Fourth, they used the supervised learning model to estimate the sentiment of all the posts.
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.
Fourth, the researchers used the supervised learning model to estimate the sentiment of all the posts.
(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.
Third, they trained a supervised learning model to classify the sentiment of posts.
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.
Also, these projects can be done with open calls,whereby researchers compete to create machine learning models with the greatest predictive performance.
Finally, they used this machine learning model to estimate the sentiment of all 11 million posts.
At that point, researchers need to build a computer-assisted human computation system in which humanclassifications are used to train a machine learning model that can then be applied to virtually unlimited amounts of data.
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).
The research of Blumenstock and colleagues(2015) involved building a machine learning model that could use digital trace data to predict survey responses.
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.
The features in Banerji and colleagues' 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.
The research of Blumenstock and colleagues(2015) involved building a machine learning model that could use digital trace data to predict survey responses.
Further, foreshadowing a theme that will occur throughoutthis 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.
First, for the people in both data sources, build a machine learning model that uses digital trace data to predict survey answers.
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.
Then, King andcolleagues used this hand-labeled data to estimate a machine learning model that could infer the sentiment of a post based on its characteristics.
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.
There were two main technical reasons for the improvement: 1 they used more sophisticated methods(i.e.,a new approach to feature engineering and a more sophisticated machine learning model) and 2 rather than attempting to infer responses to individual survey questions(e.g.,“Do you own a radio?”), they attempted to infer a composite wealth index.