All Questions
Tagged with bias classification
24
questions
2
votes
1
answer
48
views
How normalizing data cause not problem in prediction?
In algorithms that perform better with data normalization or deep learning problems such as classification, how normalizing data does not bias our algorithm? I mean, in training or even testing, we ...
- 131
1
vote
1
answer
18
views
Correction of labelling bias using the labeler identity as a feature
Suppose I have a dataset labeled by multiple analysts.
I assume that each analyst has some bias in his labeling.
Is there any literature on reducing the bias effect on the general model by using the ...
- 390
1
vote
1
answer
28
views
Manually adding edge-cases to a text classification model
Suppose I want to get training data for a model that deals with sentiment analysis for text that indicates an affirmative (yes) or negative (no) response, such as
...
0
votes
0
answers
252
views
Leave One Subject Out Cross Validation: mean vs median
Assume we have a dataset with n subjects and m labels and train a classifier. To ensure that there is no subject bias in the ...
- 121
3
votes
1
answer
909
views
Why do we need separate data for probability calibration?
Why do we need separate data for probability calibration?
Scikit learn documentation says:
The samples that are used to fit the calibrator should not be the same samples used to fit the classifier, as ...
- 485
0
votes
1
answer
54
views
How to account for known bias in classification data
I apologize for the vagueness beforehand. Here's my experimental setup. I am trying to see if a data point has a property p. For example, in an image classification ...
- 424
3
votes
1
answer
159
views
Random forest classifier. Some of my data is overrepresented. Is this an issue?
I am using a random forest classifier to predict plant color in my study species, using a variety of environmental variables. My data comes from citizen scientists and I am worried that the class ...
- 41
0
votes
0
answers
67
views
Bias and variance of an estimator of a model mean
I have a binary classification model and I need to use its output to estimate the means of groups of observations. I have two questions:
A. Can I compute the the bias and variance of the estimator of ...
- 111
1
vote
1
answer
148
views
Why does k-means have more bias than spectral clustering and GMM?
I ran into a 2019-Entrance Exam question as follows:
Which of the following algorithm has the higher bias?
GMM
GMM (identity covariance matrix)
spectral clustering
k-means
The answer mentioned is (...
- 11
2
votes
1
answer
4k
views
Why does increasing K increase bias and reduce variance
I get confused when it comes to KNN, why exactly does increasing K increase bias and reduce variance
Correct me if I’m wrong
My knowledge, suppose we have a
regression problem
If k=1
and our nearest ...
- 141
5
votes
1
answer
2k
views
Definition of Bias and Variance in classification problems
I was looking into a StatQuest video and he gave the meaning of bias and variance in regression problems
Correct me if I’m wrong
Bias is the sum of squares error between the predicted and actual ...
- 141
1
vote
0
answers
54
views
Diffrence between bias and training error regarding to KNN
So I'll ask my question by presenting another question,
Which of the following statements regarding the k-nearest neighbors classifier for samples in $\mathcal{X} =
\mathbb{R}^d$ is true?
(a) The ...
- 11
8
votes
4
answers
316
views
Was Amazon's AI tool, more than human recruiters, biased against women?
A typical example how bias in data is being copied by AI is Amazon's recruiting tool that got abandoned in 2018.
In the various reports it is implicitly (or sometimes explicitly) stated that the AI ...
- 82.2k
1
vote
1
answer
266
views
No need for bias term if data is standardised? Linear classification models
For linear classification models, e.g. perceptron, bias term allows to move separating hyperplane away from origin. If data is scattered around the zero does that mean that we don't need bias term?
- 59
3
votes
0
answers
93
views
Calibrating probabilities of a binary classifier when class prior is unknown
Is it possible to calibrate the probabilities of a binary classifier when the class priors are unknown?
In cases where the data is obtained with selection bias (i.e. more positives than negatives in ...
- 83