Imbalanced dataset binary classification The 2019 Stack Overflow Developer Survey Results Are InAre unbalanced datasets problematic, and (how) does oversampling (purport to) help?Imbalanced data classification using boosting algorithmsBinary classification in imbalanced dataClassification algorithms for handling Imbalanced data setsWhat is the effect of training a model on an imbalanced dataset & using it on a balanced dataset?imbalanced binary classification with skewed featuresCross validation and imbalanced learningimbalanced datasetcross validation gives wrong resultsData augmentation or weighted loss function for imbalanced classes?Handling imbalanced data for classification
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Imbalanced dataset binary classification
The 2019 Stack Overflow Developer Survey Results Are InAre unbalanced datasets problematic, and (how) does oversampling (purport to) help?Imbalanced data classification using boosting algorithmsBinary classification in imbalanced dataClassification algorithms for handling Imbalanced data setsWhat is the effect of training a model on an imbalanced dataset & using it on a balanced dataset?imbalanced binary classification with skewed featuresCross validation and imbalanced learningimbalanced datasetcross validation gives wrong resultsData augmentation or weighted loss function for imbalanced classes?Handling imbalanced data for classification
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
I am new in ML & DS and i have a dataset with an imbalance of 9:1 for Binary Classification,as an assignment. Could you please guide me in this regard? Also Which classifier is best for Imbalanced Binary Classification?
Regrds.
machine-learning classification binary-data unbalanced-classes
asked 2 days ago
Sid_MirzaSid_Mirza
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am new in ML & DS and i have a dataset with an imbalance of 9:1 for Binary Classification,as an assignment. Could you please guide me in this regard? Also Which classifier is best for Imbalanced Binary Classification?
Regrds.
machine-learning classification binary-data unbalanced-classes
asked 2 days ago
Sid_MirzaSid_Mirza
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
2 days ago
add a comment |
$begingroup$
I am new in ML & DS and i have a dataset with an imbalance of 9:1 for Binary Classification,as an assignment. Could you please guide me in this regard? Also Which classifier is best for Imbalanced Binary Classification?
Regrds.
machine-learning classification binary-data unbalanced-classes
asked 2 days ago
Sid_MirzaSid_Mirza
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am new in ML & DS and i have a dataset with an imbalance of 9:1 for Binary Classification,as an assignment. Could you please guide me in this regard? Also Which classifier is best for Imbalanced Binary Classification?
Regrds.
machine-learning classification binary-data unbalanced-classes
machine-learning classification binary-data unbalanced-classes
asked 2 days ago
Sid_MirzaSid_Mirza
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sid_MirzaSid_Mirza
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sid_MirzaSid_Mirza
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sid_MirzaSid_Mirza
112
asked 2 days ago
Sid_MirzaSid_Mirza
112
112
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sid_Mirza is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
2 days ago
add a comment |
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
2 days ago
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
2 days ago
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
2 days ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You got off on the wrong foot by conceptualizing this as a classification problem. The fact that $Y$ is binary has nothing to do with trying to make classifications. And when the balance of $Y$ is far from 1:1 you need to think about modeling tendencies for $Y$, not modeling $Y$. In other words, the appropriate task is to estimate $P(Y=1 | X)$ using a model such as the binary logistic regression model. The logistic model is a direct probability estimator. Details may be found here and here.
Once you have a validated probability model and a utility/cost/loss function you can generate optimum decisions. The probabilities help to trade off the consequences of wrong decisions.
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
$endgroup$
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
add a comment |
Your Answer
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
You got off on the wrong foot by conceptualizing this as a classification problem. The fact that $Y$ is binary has nothing to do with trying to make classifications. And when the balance of $Y$ is far from 1:1 you need to think about modeling tendencies for $Y$, not modeling $Y$. In other words, the appropriate task is to estimate $P(Y=1 | X)$ using a model such as the binary logistic regression model. The logistic model is a direct probability estimator. Details may be found here and here.
Once you have a validated probability model and a utility/cost/loss function you can generate optimum decisions. The probabilities help to trade off the consequences of wrong decisions.
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
$endgroup$
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
add a comment |
$begingroup$
You got off on the wrong foot by conceptualizing this as a classification problem. The fact that $Y$ is binary has nothing to do with trying to make classifications. And when the balance of $Y$ is far from 1:1 you need to think about modeling tendencies for $Y$, not modeling $Y$. In other words, the appropriate task is to estimate $P(Y=1 | X)$ using a model such as the binary logistic regression model. The logistic model is a direct probability estimator. Details may be found here and here.
Once you have a validated probability model and a utility/cost/loss function you can generate optimum decisions. The probabilities help to trade off the consequences of wrong decisions.
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
$endgroup$
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
add a comment |
$begingroup$
You got off on the wrong foot by conceptualizing this as a classification problem. The fact that $Y$ is binary has nothing to do with trying to make classifications. And when the balance of $Y$ is far from 1:1 you need to think about modeling tendencies for $Y$, not modeling $Y$. In other words, the appropriate task is to estimate $P(Y=1 | X)$ using a model such as the binary logistic regression model. The logistic model is a direct probability estimator. Details may be found here and here.
Once you have a validated probability model and a utility/cost/loss function you can generate optimum decisions. The probabilities help to trade off the consequences of wrong decisions.
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
$endgroup$
You got off on the wrong foot by conceptualizing this as a classification problem. The fact that $Y$ is binary has nothing to do with trying to make classifications. And when the balance of $Y$ is far from 1:1 you need to think about modeling tendencies for $Y$, not modeling $Y$. In other words, the appropriate task is to estimate $P(Y=1 | X)$ using a model such as the binary logistic regression model. The logistic model is a direct probability estimator. Details may be found here and here.
Once you have a validated probability model and a utility/cost/loss function you can generate optimum decisions. The probabilities help to trade off the consequences of wrong decisions.
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
answered 2 days ago
Frank HarrellFrank Harrell
55.9k3110245
55.9k3110245
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
add a comment |
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Thanks Sir Frank Harrell, The dataset is in floating point values but the target is in binary form as you said 'Y'. i applied Linear Regression, Random Forests,Decision Tree and some ensemble methods but the Linear regression gave an AUC score of 78.2% whereas random forests and LightGBM performed better. Now i want to increase the AUC score. Here is the list of parameters i used for lgb:
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
params = "objective" : "binary", "metric" : "auc", "boosting": 'gbdt', "max_depth" : -1, "num_leaves" : 13, "learning_rate" : 0.01, "bagging_freq": 5, "bagging_fraction" : 0.4, "feature_fraction" : 0.05, "min_data_in_leaf": 80, "min_sum_heassian_in_leaf": 10, "tree_learner": "serial", "boost_from_average": "false", "bagging_seed" : random_state, "verbosity" : 1, "seed": random_state
$endgroup$
– Sid_Mirza
2 days ago
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Is the binary target a derivation from a floating point continuous outcome variable? If so you will need to go back to that variable and not use an information-losing dichotomization.
$endgroup$
– Frank Harrell
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
Yes, all the 100+ attributes have continuous values on the basis of which, we have to classify the target in binary form either yes or no.
$endgroup$
– Sid_Mirza
yesterday
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
$begingroup$
I assume by that you mean that the target originated as binary in its rawest form. You are still trying to cast the problem inappropriately as classification. You cannot do anything but estimate tendencies, nor should you. Once you have probability estimates you can make optimum decisions given the loss function.
$endgroup$
– Frank Harrell
12 hours ago
add a comment |
Sid_Mirza is a new contributor. Be nice, and check out our Code of Conduct.
Sid_Mirza is a new contributor. Be nice, and check out our Code of Conduct.
Sid_Mirza is a new contributor. Be nice, and check out our Code of Conduct.
Sid_Mirza is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
2 days ago