Need help in selecting a model for a classification problem with binary data The Next CEO of Stack Overflow2019 Community Moderator ElectionWhich non-training classification methods are available?Method for solving problem with variable number of predictorsHow to deal with a machine learning model which affects future ground truth data?Suggestions for binary classification algorithmdecision rules for each feature (binary classification)Need Advice, Classification Problem in Python: Should I use Decision tree, Random Forests, or Logistic Regression?Valid Approach to Kaggle's Porto Seguro ML Problem?Keras LSTM model for binary classification with sequencesBinary classification model with time series as variablesData splitting for a binary classification model
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Need help in selecting a model for a classification problem with binary data
The Next CEO of Stack Overflow2019 Community Moderator ElectionWhich non-training classification methods are available?Method for solving problem with variable number of predictorsHow to deal with a machine learning model which affects future ground truth data?Suggestions for binary classification algorithmdecision rules for each feature (binary classification)Need Advice, Classification Problem in Python: Should I use Decision tree, Random Forests, or Logistic Regression?Valid Approach to Kaggle's Porto Seguro ML Problem?Keras LSTM model for binary classification with sequencesBinary classification model with time series as variablesData splitting for a binary classification model
$begingroup$
I have a clinical data set. The goal is to develop a model that predicts race based on binary data, whether or not a gene is present.
I am struggling to find a classification model that works without continuous data. One model that might work is k-modes. Are there any other models that I should consider?
classification
asked Mar 1 at 20:59
m.seludom.seludo
1
$endgroup$
bumped to the homepage by Community♦ 8 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have a clinical data set. The goal is to develop a model that predicts race based on binary data, whether or not a gene is present.
I am struggling to find a classification model that works without continuous data. One model that might work is k-modes. Are there any other models that I should consider?
classification
asked Mar 1 at 20:59
m.seludom.seludo
1
$endgroup$
bumped to the homepage by Community♦ 8 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have a clinical data set. The goal is to develop a model that predicts race based on binary data, whether or not a gene is present.
I am struggling to find a classification model that works without continuous data. One model that might work is k-modes. Are there any other models that I should consider?
classification
asked Mar 1 at 20:59
m.seludom.seludo
1
$endgroup$
I have a clinical data set. The goal is to develop a model that predicts race based on binary data, whether or not a gene is present.
I am struggling to find a classification model that works without continuous data. One model that might work is k-modes. Are there any other models that I should consider?
classification
classification
asked Mar 1 at 20:59
m.seludom.seludo
1
asked Mar 1 at 20:59
m.seludom.seludo
1
asked Mar 1 at 20:59
m.seludom.seludo
1
asked Mar 1 at 20:59
m.seludom.seludo
1
asked Mar 1 at 20:59
m.seludom.seludo
1
1
bumped to the homepage by Community♦ 8 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 8 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
You might want to consider decision trees or random forests, those classifiers can work with non-continuous data, and actually are really good.
They are implemented in scikit-learn.
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
$endgroup$
add a comment |
$begingroup$
Logistic regression models can certainly be used with dichotomous features. It also provides coefficient estimates for each feature so that relationships between features and the target labels can be tested and interpreted. Predictions made from logistic regression models are probabilities rather than binary decisions which can be helpful if you have targets for Type II Error rates, False Omission rates, etc. which is often the case in clinical data.
Distance-based methods can also be used such as the k-nearest neighbor algorithm. With all binary features, it would make sense to use distance measures designed for dichotomous data, such as the Russell Rao distance metric. These models will make predictions based on the class labels of the k-nearest observations in the feature space.
answered Mar 2 at 2:53
silent_specsilent_spec
11
$endgroup$
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
add a comment |
$begingroup$
I recommend using LDA (Latent Dirichlet allocation) which works efficiently with discrete data.
answered Mar 2 at 4:45
pythinkerpythinker
465129
$endgroup$
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
You might want to consider decision trees or random forests, those classifiers can work with non-continuous data, and actually are really good.
They are implemented in scikit-learn.
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
$endgroup$
add a comment |
$begingroup$
You might want to consider decision trees or random forests, those classifiers can work with non-continuous data, and actually are really good.
They are implemented in scikit-learn.
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
$endgroup$
add a comment |
$begingroup$
You might want to consider decision trees or random forests, those classifiers can work with non-continuous data, and actually are really good.
They are implemented in scikit-learn.
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
$endgroup$
You might want to consider decision trees or random forests, those classifiers can work with non-continuous data, and actually are really good.
They are implemented in scikit-learn.
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
answered Mar 1 at 21:15
Antonio JurićAntonio Jurić
741111
741111
add a comment |
add a comment |
$begingroup$
Logistic regression models can certainly be used with dichotomous features. It also provides coefficient estimates for each feature so that relationships between features and the target labels can be tested and interpreted. Predictions made from logistic regression models are probabilities rather than binary decisions which can be helpful if you have targets for Type II Error rates, False Omission rates, etc. which is often the case in clinical data.
Distance-based methods can also be used such as the k-nearest neighbor algorithm. With all binary features, it would make sense to use distance measures designed for dichotomous data, such as the Russell Rao distance metric. These models will make predictions based on the class labels of the k-nearest observations in the feature space.
answered Mar 2 at 2:53
silent_specsilent_spec
11
$endgroup$
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
add a comment |
$begingroup$
Logistic regression models can certainly be used with dichotomous features. It also provides coefficient estimates for each feature so that relationships between features and the target labels can be tested and interpreted. Predictions made from logistic regression models are probabilities rather than binary decisions which can be helpful if you have targets for Type II Error rates, False Omission rates, etc. which is often the case in clinical data.
Distance-based methods can also be used such as the k-nearest neighbor algorithm. With all binary features, it would make sense to use distance measures designed for dichotomous data, such as the Russell Rao distance metric. These models will make predictions based on the class labels of the k-nearest observations in the feature space.
answered Mar 2 at 2:53
silent_specsilent_spec
11
$endgroup$
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
add a comment |
$begingroup$
Logistic regression models can certainly be used with dichotomous features. It also provides coefficient estimates for each feature so that relationships between features and the target labels can be tested and interpreted. Predictions made from logistic regression models are probabilities rather than binary decisions which can be helpful if you have targets for Type II Error rates, False Omission rates, etc. which is often the case in clinical data.
Distance-based methods can also be used such as the k-nearest neighbor algorithm. With all binary features, it would make sense to use distance measures designed for dichotomous data, such as the Russell Rao distance metric. These models will make predictions based on the class labels of the k-nearest observations in the feature space.
answered Mar 2 at 2:53
silent_specsilent_spec
11
$endgroup$
Logistic regression models can certainly be used with dichotomous features. It also provides coefficient estimates for each feature so that relationships between features and the target labels can be tested and interpreted. Predictions made from logistic regression models are probabilities rather than binary decisions which can be helpful if you have targets for Type II Error rates, False Omission rates, etc. which is often the case in clinical data.
Distance-based methods can also be used such as the k-nearest neighbor algorithm. With all binary features, it would make sense to use distance measures designed for dichotomous data, such as the Russell Rao distance metric. These models will make predictions based on the class labels of the k-nearest observations in the feature space.
answered Mar 2 at 2:53
silent_specsilent_spec
11
answered Mar 2 at 2:53
silent_specsilent_spec
11
answered Mar 2 at 2:53
silent_specsilent_spec
11
answered Mar 2 at 2:53
silent_specsilent_spec
11
11
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
add a comment |
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
$begingroup$
Thanks, I will look into those models.
$endgroup$
– m.seludo
Mar 2 at 3:46
add a comment |
$begingroup$
I recommend using LDA (Latent Dirichlet allocation) which works efficiently with discrete data.
answered Mar 2 at 4:45
pythinkerpythinker
465129
$endgroup$
add a comment |
$begingroup$
I recommend using LDA (Latent Dirichlet allocation) which works efficiently with discrete data.
answered Mar 2 at 4:45
pythinkerpythinker
465129
$endgroup$
add a comment |
$begingroup$
I recommend using LDA (Latent Dirichlet allocation) which works efficiently with discrete data.
answered Mar 2 at 4:45
pythinkerpythinker
465129
$endgroup$
I recommend using LDA (Latent Dirichlet allocation) which works efficiently with discrete data.
answered Mar 2 at 4:45
pythinkerpythinker
465129
answered Mar 2 at 4:45
pythinkerpythinker
465129
answered Mar 2 at 4:45
pythinkerpythinker
465129
answered Mar 2 at 4:45
pythinkerpythinker
465129
465129
add a comment |
add a comment |
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