Unsupervised Learning and Training Data
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As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by using this relation, our learning system tries to predict labels of data samples in next data sets.
However, there is no need to find out such a relation in unsupervised learning because the data samples do not have labels; they only consist of features. In this case, do we need a training set in unsupervised learning ?
machine-learning training unsupervised-learning supervised-learning
asked 9 hours ago
GoktugGoktug
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As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by using this relation, our learning system tries to predict labels of data samples in next data sets.
However, there is no need to find out such a relation in unsupervised learning because the data samples do not have labels; they only consist of features. In this case, do we need a training set in unsupervised learning ?
machine-learning training unsupervised-learning supervised-learning
asked 9 hours ago
GoktugGoktug
101
New contributor
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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$begingroup$
As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by using this relation, our learning system tries to predict labels of data samples in next data sets.
However, there is no need to find out such a relation in unsupervised learning because the data samples do not have labels; they only consist of features. In this case, do we need a training set in unsupervised learning ?
machine-learning training unsupervised-learning supervised-learning
asked 9 hours ago
GoktugGoktug
101
New contributor
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by using this relation, our learning system tries to predict labels of data samples in next data sets.
However, there is no need to find out such a relation in unsupervised learning because the data samples do not have labels; they only consist of features. In this case, do we need a training set in unsupervised learning ?
machine-learning training unsupervised-learning supervised-learning
machine-learning training unsupervised-learning supervised-learning
asked 9 hours ago
GoktugGoktug
101
New contributor
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 hours ago
GoktugGoktug
101
New contributor
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 hours ago
GoktugGoktug
101
New contributor
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 hours ago
GoktugGoktug
101
asked 9 hours ago
GoktugGoktug
101
101
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Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Goktug is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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1 Answer
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In unsupervised learning,the learning procedure is finding similarity between training samples, and putting similar items into a same cluster,
training phase in unsupervised learning produce some sets with similar items.
Then,in test phase the similarity is calculated for all items of each set, and check if the test item is similar to each cluster's items or not, if it was similar to at least one item, the test item belongs to that cluster.
SO... YES, we need training set and test set, the training set helps to find thresholds to find similarity
answered 8 hours ago
SahaSaha
266
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1 Answer
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oldest
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1 Answer
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active
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active
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$begingroup$
In unsupervised learning,the learning procedure is finding similarity between training samples, and putting similar items into a same cluster,
training phase in unsupervised learning produce some sets with similar items.
Then,in test phase the similarity is calculated for all items of each set, and check if the test item is similar to each cluster's items or not, if it was similar to at least one item, the test item belongs to that cluster.
SO... YES, we need training set and test set, the training set helps to find thresholds to find similarity
answered 8 hours ago
SahaSaha
266
$endgroup$
add a comment |
$begingroup$
In unsupervised learning,the learning procedure is finding similarity between training samples, and putting similar items into a same cluster,
training phase in unsupervised learning produce some sets with similar items.
Then,in test phase the similarity is calculated for all items of each set, and check if the test item is similar to each cluster's items or not, if it was similar to at least one item, the test item belongs to that cluster.
SO... YES, we need training set and test set, the training set helps to find thresholds to find similarity
answered 8 hours ago
SahaSaha
266
$endgroup$
add a comment |
$begingroup$
In unsupervised learning,the learning procedure is finding similarity between training samples, and putting similar items into a same cluster,
training phase in unsupervised learning produce some sets with similar items.
Then,in test phase the similarity is calculated for all items of each set, and check if the test item is similar to each cluster's items or not, if it was similar to at least one item, the test item belongs to that cluster.
SO... YES, we need training set and test set, the training set helps to find thresholds to find similarity
answered 8 hours ago
SahaSaha
266
$endgroup$
In unsupervised learning,the learning procedure is finding similarity between training samples, and putting similar items into a same cluster,
training phase in unsupervised learning produce some sets with similar items.
Then,in test phase the similarity is calculated for all items of each set, and check if the test item is similar to each cluster's items or not, if it was similar to at least one item, the test item belongs to that cluster.
SO... YES, we need training set and test set, the training set helps to find thresholds to find similarity
answered 8 hours ago
SahaSaha
266
edited 8 hours ago
answered 8 hours ago
SahaSaha
266
answered 8 hours ago
SahaSaha
266
answered 8 hours ago
SahaSaha
266
266
add a comment |
add a comment |
Goktug is a new contributor. Be nice, and check out our Code of Conduct.
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