Imbalanced dataset binary classification The 2019 Stack Overflow Developer Survey Results Are In Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar ManaraAre 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
Multi tool use
Keeping a retro style to sci-fi spaceships?
How to support a colleague who finds meetings extremely tiring?
What happens to a Warlock's expended Spell Slots when they gain a Level?
Match Roman Numerals
What was the last x86 CPU that did not have the x87 floating-point unit built in?
How to handle characters who are more educated than the author?
Single author papers against my advisor's will?
How to make Illustrator type tool selection automatically adapt with text length
Is it ok to offer lower paid work as a trial period before negotiating for a full-time job?
Didn't get enough time to take a Coding Test - what to do now?
Word for: a synonym with a positive connotation?
Example of compact Riemannian manifold with only one geodesic.
Is there a way to generate uniformly distributed points on a sphere from a fixed amount of random real numbers per point?
What other Star Trek series did the main TNG cast show up in?
Do warforged have souls?
How to determine omitted units in a publication
Why doesn't a hydraulic lever violate conservation of energy?
Why are PDP-7-style microprogrammed instructions out of vogue?
Deal with toxic manager when you can't quit
60's-70's movie: home appliances revolting against the owners
Was credit for the black hole image misappropriated?
Word to describe a time interval
Identify 80s or 90s comics with ripped creatures (not dwarves)
Is every episode of "Where are my Pants?" identical?
Imbalanced dataset binary classification
The 2019 Stack Overflow Developer Survey Results Are In
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar ManaraAre 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 Apr 8 at 10:31
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 Apr 8 at 10:31
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
Apr 8 at 19:10
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 Apr 8 at 10:31
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 Apr 8 at 10:31
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 Apr 8 at 10:31
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 Apr 8 at 10:31
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 Apr 8 at 10:31
Sid_MirzaSid_Mirza
112
asked Apr 8 at 10:31
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
Apr 8 at 19:10
add a comment |
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
Apr 8 at 19:10
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
Apr 8 at 19:10
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
Apr 8 at 19:10
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 Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
$endgroup$
$begingroup$
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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
2 days ago
add a comment |
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "65"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sid_Mirza is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f401800%2fimbalanced-dataset-binary-classification%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
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 Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
$endgroup$
$begingroup$
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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
2 days 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 Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
$endgroup$
$begingroup$
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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
2 days 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 Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
$endgroup$
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 Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
answered Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
answered Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
answered Apr 8 at 11:59
Frank HarrellFrank Harrell
56k3110245
56k3110245
$begingroup$
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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
2 days 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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:18
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 8 at 17:21
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 4:11
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
Apr 9 at 19:29
$begingroup$
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
2 days 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
2 days 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.
Thanks for contributing an answer to Cross Validated!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f401800%2fimbalanced-dataset-binary-classification%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
MBscIdCZsGRSq,5YcCD,2JIt6 CejFsLE5oKsnQycNB
$begingroup$
Related: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
$endgroup$
– Stephan Kolassa
Apr 8 at 19:10