Vote Algorithm in Weka
I would like to know how the vote algorithm works.
Suppose I am using Naive Bayes and Logistic Regression in vote.
And I am getting better accuracy than the individual algorithm.
How am I getting better accuracy and What is the working procedure??
machine-learning classification weka
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
add a comment |
I would like to know how the vote algorithm works.
Suppose I am using Naive Bayes and Logistic Regression in vote.
And I am getting better accuracy than the individual algorithm.
How am I getting better accuracy and What is the working procedure??
machine-learning classification weka
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
add a comment |
I would like to know how the vote algorithm works.
Suppose I am using Naive Bayes and Logistic Regression in vote.
And I am getting better accuracy than the individual algorithm.
How am I getting better accuracy and What is the working procedure??
machine-learning classification weka
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
I would like to know how the vote algorithm works.
Suppose I am using Naive Bayes and Logistic Regression in vote.
And I am getting better accuracy than the individual algorithm.
How am I getting better accuracy and What is the working procedure??
machine-learning classification weka
machine-learning classification weka
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
edited Nov 26 '18 at 21:42
Anony-Mousse
58.5k797162
58.5k797162
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
asked Nov 25 '18 at 16:13
sazzadul islam prottashasazzadul islam prottasha
11
11
add a comment |
add a comment |
1 Answer
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votes
Voting is one of the ensemble methods that allows to improve accuracy by means of combining several base classifiers. There are several possible ways to organize voting procedure: majority voting, average of probabilities, median of probabilities, etc.
For example, in case of majority voting with 10 base classifiers, the class with maximum votes is chosen among all classification results of these 10 base classifiers.
If we assume that errors of our base classifiers are independent then given n independent observations Z1, Z2, ..., ZN, each with variance 
, the variance of the mean of these observations is given by
As a result, majority voting works since it reduces the variance error components of the classifiers from the vote.
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Voting is one of the ensemble methods that allows to improve accuracy by means of combining several base classifiers. There are several possible ways to organize voting procedure: majority voting, average of probabilities, median of probabilities, etc.
For example, in case of majority voting with 10 base classifiers, the class with maximum votes is chosen among all classification results of these 10 base classifiers.
If we assume that errors of our base classifiers are independent then given n independent observations Z1, Z2, ..., ZN, each with variance , the variance of the mean of these observations is given by
As a result, majority voting works since it reduces the variance error components of the classifiers from the vote.
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
add a comment |
Voting is one of the ensemble methods that allows to improve accuracy by means of combining several base classifiers. There are several possible ways to organize voting procedure: majority voting, average of probabilities, median of probabilities, etc.
For example, in case of majority voting with 10 base classifiers, the class with maximum votes is chosen among all classification results of these 10 base classifiers.
If we assume that errors of our base classifiers are independent then given n independent observations Z1, Z2, ..., ZN, each with variance , the variance of the mean of these observations is given by
As a result, majority voting works since it reduces the variance error components of the classifiers from the vote.
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
add a comment |
Voting is one of the ensemble methods that allows to improve accuracy by means of combining several base classifiers. There are several possible ways to organize voting procedure: majority voting, average of probabilities, median of probabilities, etc.
For example, in case of majority voting with 10 base classifiers, the class with maximum votes is chosen among all classification results of these 10 base classifiers.
If we assume that errors of our base classifiers are independent then given n independent observations Z1, Z2, ..., ZN, each with variance , the variance of the mean of these observations is given by
As a result, majority voting works since it reduces the variance error components of the classifiers from the vote.
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
Voting is one of the ensemble methods that allows to improve accuracy by means of combining several base classifiers. There are several possible ways to organize voting procedure: majority voting, average of probabilities, median of probabilities, etc.
For example, in case of majority voting with 10 base classifiers, the class with maximum votes is chosen among all classification results of these 10 base classifiers.
If we assume that errors of our base classifiers are independent then given n independent observations Z1, Z2, ..., ZN, each with variance , the variance of the mean of these observations is given by
As a result, majority voting works since it reduces the variance error components of the classifiers from the vote.
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
answered Nov 25 '18 at 17:16
Michael GlazunovMichael Glazunov
10113
10113
add a comment |
add a comment |
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