Examples of using Supervised learning in English and their translations into Slovenian
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Programming
For more on supervised learning, see James et al.
Reliability estimation of individual predictions in supervised learning.
Supervised Learning: Classification and regression¶.
There are two broad types of machine learning- supervised learning and unsupervised learning. .
Then, the supervised learning model was used to impute the survey responses for everyone.
There are two learning types in machine learning- supervised learning and unsupervised learning. .
Unsupervised learning is comparatively difficult with respect to supervised learning.
In machine learning, multiple-instance learning(MIL) is a type of supervised learning.
For more on supervised learning, see James et al.
Multiple-instance learning In machine learning, multiple-instance learning(MIL)is a variation on supervised learning.
Fourth, they used the supervised learning model to estimate the sentiment of all the posts.
In machine learning, this approach- using labeled examples to create a model that can then label new data-is called supervised learning.
Third, they trained a supervised learning model to classify the sentiment of posts.
This might sound magical, but the approach Kosinski and colleagues used- which combines digital traces,surveys, and supervised learning- is actually something that I have already told you about.
Then, the supervised learning model was used to impute the survey responses for all 1.5 million customers.
This sounds like a massive job, but they solved it using a powerful trick that is common in data science butrelatively rare in social science: supervised learning; see figure 2.5.
Next, the researchers built a supervised learning model to predict the survey responses from the person by feature matrix.
Further, foreshadowing a theme that will occur throughout the book, these latent-attribute inference problems-which can sometimes be solved with supervised learning- turn out to be very common in social research in the digital age.
Next, the researchers built a supervised learning model to predict the survey responses from the person-by-feature matrix.
Thus, rather than manually reading and labeling 11 million posts- which would be logistically impossible- King and colleagues manually labeled a small number of posts andthen used supervised learning to estimate the sentiment of all the posts.
A supervised learning algorithm analyses the training data and produces an inferred function, which can be used for mapping new examples.
These are tagged with morpho-syntactic descriptions of particular words,and therefore the algorithm for supervised learning which creates a knowledge model on the basis of the tagged vectors can be used.
Next, in the supervised learning step, Blumenstock built a model to predict the survey response for each person based on their features.
Thus, rather than manually reading and labeling 11 million posts- which would be logistically impossible- King and colleagues manually labeled a small number of posts andthen used supervised learning to estimate the sentiment of all the posts.
Next, in the supervised learning step, Blumenstock built a statistical model to predict the survey response for each person based on their features.
Thus, rather than manually reading and labeling 11 million posts(which would be logistically impossible), they manually labeled a small number of posts andthen used what data scientists would call supervised learning to estimate the categories of all the posts.
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.