Imbalanced dataset binary classificationAre unbalanced datasets problematic, and (how) does oversampling...
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Imbalanced dataset binary classification
Are 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;
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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 19 hours 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 19 hours 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
10 hours 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 19 hours 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 19 hours 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 19 hours 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 19 hours 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 19 hours ago
Sid_MirzaSid_Mirza
112
asked 19 hours 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
10 hours ago
add a comment |
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
10 hours ago
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
10 hours ago
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
10 hours 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 17 hours 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
12 hours 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
12 hours 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
1 hour 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 17 hours 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
12 hours 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
12 hours 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
1 hour 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 17 hours 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
12 hours 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
12 hours 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
1 hour 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 17 hours 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 17 hours ago
Frank HarrellFrank Harrell
55.9k3110245
answered 17 hours ago
Frank HarrellFrank Harrell
55.9k3110245
answered 17 hours ago
Frank HarrellFrank Harrell
55.9k3110245
answered 17 hours 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
12 hours 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
12 hours 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
1 hour 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
12 hours 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
12 hours 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
1 hour 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
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
12 hours 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
12 hours 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
12 hours 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
1 hour 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
1 hour 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
10 hours ago