Linked Questions

2
votes
0answers
644 views

How to fit Decision Tree classifier for highly imbalanced response variable? [duplicate]

I use R, Party package in order to fit prediction model ("classifier") for "Converted.clicks" as response variable. The rest of vars are used as explaining variables in the model. Here is the ...
0
votes
0answers
22 views

Is it OK to sample based on the DV? [duplicate]

I have a dataset with a binary outcome, y. I'd like to run a decision tree on the data, but y == T is very rare, and so every leaf of the tree predicts y == F. Is there any problem with sampling ...
145
votes
9answers
61k views

Why is accuracy not the best measure for assessing classification models?

This is a general question that was asked indirectly multiple times in here, but it lacks a single authoritative answer. It would be great to have a detailed answer to this for the reference. ...
74
votes
6answers
17k views

When is unbalanced data really a problem in Machine Learning?

We already had multiple questions about unbalanced data when using logistic regression, SVM, decision trees, bagging and a number of other similar questions, what makes it a very popular topic! ...
9
votes
2answers
22k views

Is Random Forest a good option for unbalanced data Classification? [closed]

Despite the resembling and other increasing data variability approaches, can the random forest "as an algorithm" be considered a good option for the unbalanced data classification?
8
votes
3answers
4k views

Choosing a classification performance metric for model selection, feature selection, and publication

I have a small, unbalanced data set (70 positive, 30 negative), and I have been playing around with model selection for SVM parameters using BAC (balanced accuracy) and AUC (area under the curve). I ...
3
votes
1answer
12k views

R - Classification ctree {party} - Testing sample and leaf attribution with unbalanced data

Let's start with data description of the website visits I analyse : 6M rows Dependant variable quotation is binary and takes values ...
8
votes
4answers
1k views

Flexible version of logistic regression

I'm trying to fit a logistic regression where there is a huge difference in the number of data points in either group (70 Vs 10,000). A statistician friend of mine has told me that this is a known ...
6
votes
2answers
3k views

CART (rpart) balanced vs. unbalanced dataset

I am fitting a tree (CART) to the olives-dataset. The training data has 436 observations (test data: 136). I have 3 responses (the 'Region' variable) which splits the training data into 116 / 74 / 246 ...
5
votes
2answers
585 views

Prediction problem: Do I have to sample the data set so that the outcomes are balanced?

I want to predict whether a loan is default or fully paid, with about 20 features and 10,000 historical observations. Among the data over 85% are fully paid, 15% are default, I want to try ...
2
votes
1answer
768 views

Effects of selection bias in training data introduced by previous model outputs

I am developing a random forest for a binary classification problem where the trained data is heavily skewed towards one class (90% is class A and 10% is class B). The model scores data points based ...
2
votes
2answers
263 views

How to deal with data having huge disparity in number in each class

I have data in which the number of negative cases in response is approximately 98% of the total sample size (total # records are approximately 1 million, Response is ...
2
votes
1answer
683 views

Balanced accuracy for decisions trees with unbalanced data

I have a question concerning decision trees and unbalanced data. My dependent variable accounts for around 2% of the entire dataset and is binary (0 or 1). Here are the steps I follow: Note that I'm ...