Average precision score too high looking at the confusion matrix

How do I apply RandomUnderSalmpling and OverSampling in StratifiedKfoldCrossValidation?

Deep Learning Image Detection - Help needed deciphering machine learning loss and accuracy graph and finding solutions to fix model

Sklearn Pipelines + GridsearchCV + XGBoost + Learning Curve

Normalizing the confusion matrix

The reason of different results of KNN algorithm from PYOD & Sklearn packages

How to use "is_unbalance" and "scale_pos_weight" parameters in LightGBM for a binary classification project that is unbalanced (80:20)

Oversampling effect on the accuracy of a neural network model

Handling imbalanced object dataset using SMOTE technique

binary search tree_ how to update and calculate the imbalance_python

Tunning (Optuna) RandomForest Model but Give "Returned Nan" Result When Using class_weight Parameter

Get error: unexpected keyword argument 'random_state' when using TomekLinks

using SMOTE to treat imbalanced 3d array data

focal loss NLP/text data pytorch - improving results

Warning Message in binary classification model Gaussian Naive Bayes?

XGBoost Classification with highly unbalanced dataset

Why confusion matrix shows different results from random undersampling class distribution?

matplotlib: histogram of SMOTEd class distribution showing colored synthetic region

Stratified train-test splitting a Tensorflow dataset

Using Focal Loss for imbalanced dataset in PyTorch

Predictions stuck at zero when positive label (1) is only 16% of data

How can I apply different weights for my loss funciton based on the ones coming from my train_dataloader method in Pytorch Lightning?

Multi-Class imbalance with a 3D array

Use of smoteRegress R package in python using rpy2

Multiclass Sampling Strategy

AUC-ROC value greater than 1 for XGBoost classifier

How to randomise the rebalancing of a dataset

oversampling (SMOTE) does not work properly when fitted inside a pipeline

Evaluate imbalanced multi-label classification

How to handle imbalanced data in general

samples with almost identical features but different classes and poor classification preformance(recall and precision)

True Negatives have better prediction than True Positives

How can I resolve imbalanced datasets for AutoML classification on GCP?

AUROC for imbalanced dataset

Does model underfitting based-on Accuracy matter for imbalanced data?

Should we actively use the weight argument in loss functions

Multi Label Imbalanced dataset classification

How to apply SMOTE on multivarite time series data?

Binary data however oversampler states it is multilabeled

Imbalanced classes for binary classification affecting metrics during testing

TypeError: fit_resample() missing 1 required positional argument: 'y'

Preparation of rainfall data for classification algorithms for quantitative rainfall forecasting

Error: Not a recognized resampling method

How does CfsSubsetEva (Correlation-based Feature Selection) works in Weka

Issue with NRSBoundary_SMOTE (KeyError: 0)

Handling Imbalanced Data with Large Dataset

Augmenting minority class only in an unbalanced dataset

How do I know the order of the classes in a CatBoost classifier weights?

Detect data imbalance in Python

Why the equivalent class_weights for Logistic Regression in sklearn generates different outcomes?

The length of dataset is different from the length of labels and data when creating imbalanced dataset using cifar10

R Tuning Binary Prediction Threshold

Difference between class weights and case weights in R?

how to construct stratified tensorflow dataset?

Tidymodels class cost

Python / Imblearn : Any ways to reduce computation time of undersampling?

R Multilevel Prediction in Tidymodels with Imbalanced Nested Data

Cannot import name 'available_if' from 'sklearn.utils.metaestimators'

class_weight attribute of DecisionTreeClassifier having no effect on confusion matrix, recall

How to solve the wrong variable type error when handling imbalance dataset by ROSE in R?

Could an array of word vectors be handled if use imbalanced data techniques?

SMOTE - AttributeError: 'numpy.ndarray' object has no attribute 'value_counts'

R: Error in model.frame.default(formula = class ~ step + type + amount + :) : object is not a matrix

Imbalanced Dataset Classification using Keras

How to deal with highly imbalanced samples for XGBoost properly?

Which performance metrics (F1 Score, ROC AUC, PRC, MCC Score) can help me assess my model's performance on an imbalanced dataset?

R: how to get the same (high-quality) results from ranger using aligned setting for h2o(.ai) randomForest

SMOTE fails at times to oversample the most underrepresented class

KeyError: 0 with Safe_Level_SMOTE

Multiclass SMOTEBoost with 'overrsample all' strategy

How to balance an image dataset using Tensorflow

Error when trying to install imblearn package

Which metric to use for imbalanced classification problem?

A problem in using AIF360 metrics in my code

Split an imbalanced dataset

roc_auc_score for imbalanced classes

Should I choose higher AUC over higher accuracy

Balanced batch generator returns inconsistent class number

imbalanced classification using undersampling and oversampling using pytorch python

Change learning rate within minibatch - keras

Binary Classification Problem: How to Proceed With Severe Data Imbalance?

spacy - 3.1 custom loss function and data augmentation for named entity recognition for imbalanced data

How to retrieve selected feature names after using a pipeline with imblearn

Show the data which is not chosen by under sampling approach

Scikit learn Stratified Shuffle Split does not work when one of the classes has just one instance

How to use batch balancer in 1 dimension convolution

Cross-validation with class imbalance

how to import "balanced_batch_generator"?

Training, validation and testing: number of positive and negative samples per set

how to make a soft accuracy and loss curves in deep learning models

BERT classification on imbalanced or small dataset

Loss function for binary classification with problem of data imbalance

XGboost equivalent to LinearLearner's balance_multiclass_weights?

Distinct SVM models giving exactly the same results in R

CoxTimeVaryingFitter model for Inference

XGBoost for multiclassification and imbalanced data

Class imbalance and drop performance after usage of parameter stratify

Dealing with imbalanced classes SMOTE, still not getting great results

Calibration of output from Neural Network on imbalanced dataset

Imbalanced multiclass classification dataset: undersample or oversample?