917 views

### Why would random forest perform bad on unbalanced class

There is a huge number of posts saying that an imbalanced classes are bad. And only half explains it in terms of recall-presicion scores, meaning that accuracy can be high but F1 score low. What I ...
491 views

### Calibration after up and downsampling

I am experimenting with different techniques to deal with imbalanced classes in a classification problem. I am comparing upsampling the minority class with downsampling the majority class. Furthermore ...
337 views

### Effects of class imbalance on nn batch training

Say I have a binary classification task, where the positive class (1) is only 1% of the whole data set. Intuitively I can understand why this could be bad for the classifier as the model may learn ...
223 views

### Imbalanced Test Data

I have an imbalanced (1:5) training and test set with only two classes and have oversampled the training set with SMOTE so that the class ratio is 1:1. The ML model gives values over 0.7 for accuracy, ...
154 views

### How to deal with a highly unbalanced classification problem?

I currently have data where there are 5 labels, 1,2,3,4,5 for my $Y$ variable and a set of associated predictors $X$. The problem is, I have around $10000$ observations with label $1$ and around ...
223 views

### How to work with unbalanced datasets in generalized Boosted Regression Models using gbm in r?

I'm using generalized boosted regression models to explore what is the contribution of 20 independent environmental variables (x1, x2, ...., x20) to the explanation of the variability of the dependent ...
198 views

### When to use stratified k-fold

According to a post on Analytics Vidhya: Having said that, if the train set does not adequately represent the entire population, then using a stratified k-fold might not be the best idea. In such ...
104 views

### oversampling in nested cross validation

Introduction I have a small mixed dataset consisting of continuous and categorical independent variables with a dichotomous dependant variable. I'm running various algorithms (neural networks, random ...
167 views

### balance_classes in H2O, but for regression? [closed]

I am training with deep learning for regression in H2O for R. My dataset is unbalanced (ie. not evenly distributed). There has been discussion on whether unbalanced datasets are an issue or not, with ...
51 views

### Machine Learning - How to Sample Test and Training Data for Rare Events

Suppose I have a data set with 1000 observations. I want to train and test a Classification Model to predict a target variable as true or false. However, in my observation set, true occurs only say 10%...
135 views

### Balanced LogLoss with XGBoost

Following the discussion on here I started worrying less about class imbalance. However, I recently started building a predictor, using XGBoost, and I wanted to used LogLoss as my target metric. I ...
57 views

### How do you deal with imbalanced data when you're doing regression?

To describe my problem. I'm predicting the price of an item depending on some text and other features in an ad. The training data contains a bunch of cheap items, some medium price items and few ...
47 views

### Population / Sample question

Today we started arguing at work, and couldn't come to conclusion. Let's say that we have a population of 1000 observations about various people. 50 of these people went bankrupt (1 - bankrupt, 0 - ...