Why does a machine learning algorithm need a bias?
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The first line of section 2.7.3 in Mitchell's Machine Learning is:
"A Learner that makes no prior assumptions regarding the identity of the target concept has no rational basis for classifying any unseen instances."
Why is it that a machine learning algorithm needs a bias? Can someone please help me understand this, with examples perhaps?
machine-learning classification predictive-modeling machine-learning-model
edited yesterday
nbro
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asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
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The first line of section 2.7.3 in Mitchell's Machine Learning is:
"A Learner that makes no prior assumptions regarding the identity of the target concept has no rational basis for classifying any unseen instances."
Why is it that a machine learning algorithm needs a bias? Can someone please help me understand this, with examples perhaps?
machine-learning classification predictive-modeling machine-learning-model
edited yesterday
nbro
292417
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
$endgroup$
add a comment |
$begingroup$
The first line of section 2.7.3 in Mitchell's Machine Learning is:
"A Learner that makes no prior assumptions regarding the identity of the target concept has no rational basis for classifying any unseen instances."
Why is it that a machine learning algorithm needs a bias? Can someone please help me understand this, with examples perhaps?
machine-learning classification predictive-modeling machine-learning-model
edited yesterday
nbro
292417
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
$endgroup$
The first line of section 2.7.3 in Mitchell's Machine Learning is:
"A Learner that makes no prior assumptions regarding the identity of the target concept has no rational basis for classifying any unseen instances."
Why is it that a machine learning algorithm needs a bias? Can someone please help me understand this, with examples perhaps?
machine-learning classification predictive-modeling machine-learning-model
machine-learning classification predictive-modeling machine-learning-model
edited yesterday
nbro
292417
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
edited yesterday
nbro
292417
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
edited yesterday
nbro
292417
edited yesterday
nbro
292417
edited yesterday
nbro
292417
292417
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
asked Sep 9 '18 at 11:47
Joshua RonisJoshua Ronis
1476
1476
add a comment |
add a comment |
1 Answer
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Inductive bias means all assumptions your learning approach assumes for generalising to unseen data. What we do in machine learning is induction which means we don't have rules. By seeing data, we make rules. For example, in linear regression you may consider that the output is linear with respect to inputs, or it is polynomial. Another example can be this that in women gesture recognition, you don't care about moustaches because you know they do not have that. You can also take a look at here.
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Inductive bias means all assumptions your learning approach assumes for generalising to unseen data. What we do in machine learning is induction which means we don't have rules. By seeing data, we make rules. For example, in linear regression you may consider that the output is linear with respect to inputs, or it is polynomial. Another example can be this that in women gesture recognition, you don't care about moustaches because you know they do not have that. You can also take a look at here.
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
$endgroup$
add a comment |
$begingroup$
Inductive bias means all assumptions your learning approach assumes for generalising to unseen data. What we do in machine learning is induction which means we don't have rules. By seeing data, we make rules. For example, in linear regression you may consider that the output is linear with respect to inputs, or it is polynomial. Another example can be this that in women gesture recognition, you don't care about moustaches because you know they do not have that. You can also take a look at here.
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
$endgroup$
add a comment |
$begingroup$
Inductive bias means all assumptions your learning approach assumes for generalising to unseen data. What we do in machine learning is induction which means we don't have rules. By seeing data, we make rules. For example, in linear regression you may consider that the output is linear with respect to inputs, or it is polynomial. Another example can be this that in women gesture recognition, you don't care about moustaches because you know they do not have that. You can also take a look at here.
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
$endgroup$
Inductive bias means all assumptions your learning approach assumes for generalising to unseen data. What we do in machine learning is induction which means we don't have rules. By seeing data, we make rules. For example, in linear regression you may consider that the output is linear with respect to inputs, or it is polynomial. Another example can be this that in women gesture recognition, you don't care about moustaches because you know they do not have that. You can also take a look at here.
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
answered Sep 9 '18 at 11:56
MediaMedia
7,36562161
7,36562161
add a comment |
add a comment |
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