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How to adjust classification totals based on known bias of estimator

Let's say I have a dataset, $D$, with known ground truth labels. I nonetheless use a few-shot LLM classifier on this dataset to predict $k$ classes for each label. From the LLM results, I get ...
Learning stats by example's user avatar
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Is always low bias and low variance desirable?

Assume we have two regression models M1 and M2 for a given data. Assuming M2 has lower bias and lower variance, would you always consider using this? This example shows that if the data is random ...
chikitin's user avatar
1 vote
1 answer
77 views

How do I compute and plot Bias and Variance of a classifier in Python?

I'm new to Machine Learning and I understand bias and variance in theory but I can't seem to find a single source that explains how bias or variance can be computed. I'd like to do it in Python and ...
William's user avatar
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ML development on data biased by historical treatments

I have a dataset where in each data point was subject to certain treatment (4 different treatments) in the past based on their riskiness. The riskiness was estimated by a logistic regression model in ...
NerdyMandy's user avatar
2 votes
1 answer
174 views

Model performance impact on social discrimination?

I am currently working on a project where the data concerns people and the dataset contain personal data with sensitive attributes. (typically: age, sex, handicap, race). Now it seems there are mainly ...
Lucas Morin's user avatar
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Question about bias-variance trade off

This question is from a uni module about machine learning. I'm a bit stuck as I can't relate it to the bias-variance trade-off, to me the question implies all models have something to do with the ...
pixel.t87's user avatar
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1 answer
151 views

Neural regression predictions all around the mean of target

I have a transformer regression model and some data about last users transactions (categorical and numerical). My target has exponential distribution with mean aroud 10e4 and also zero-inflated, so I ...
CoolHumphy's user avatar
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22 views

AB testing: Control was performing 0.5% better than experiment set before the initiation of experiment

So we introduced a new feature in our app, that would aid conversion (hypothetically). When i tried to measure this incremental change in conversion, i split my base set of customers into control(C) ...
Narahari B M's user avatar
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1 answer
85 views

How do I know If my regression model is underfitting?

How do we evaluate the performance of a regression model with a certain RMSE given that a domain knowledge performance metric is not present? Maybe MAPE is one way of comparing the performance of my ...
Mehmet Deniz's user avatar
2 votes
0 answers
98 views

Bias-Variance Bulls-Eye Diagram: high variance and high bias

Often bias/variance trade-off is explained by a Bulls eye diagram. I like the explanation in the linked webpage but it doesn't answer the question how a model that has high variance and high bias ...
lordy's user avatar
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1 answer
102 views

Is Logistic Regression possible using a Convenience Sample?

I've collected some survey data on homeless individuals, surveying their drug use, education level, age, gender etc. I hope to run a logistic regression to see how impactful homelessness (+other ...
JS Holding's user avatar
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1 answer
172 views

Understanding bootstrapping in bias variance decomposition

I was going through bias and variance tradeoff article and it makes use of bias_variance_decomp function from mlxtend library. ...
Mahesha999's user avatar
3 votes
1 answer
36 views

Bias that makes annotators accept a prediction rather then coming up with a different label

Many annotation tools can speed up the classification of images (or other data) by providing a prediction of the correct label which the user can accept or correct. However, humans have a tendency to ...
moi's user avatar
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1 vote
1 answer
469 views

pytorchs LSTMs use of 'bias' and 'weight' strings

Hi I am new to RNN and have come across this the following implementation of Pytorchs LSTM, but I cant understand how (or why) the 'bias' and ...
Piskator's user avatar
  • 135
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3 answers
161 views

Proof for MSE = Var + Bias2

I am trying to prove the equality of $$\rm MSE=Var+Bias^2$$ but obviously I got something wrong as they don't equal in my calculation: So here is the example. I use monte carlo to estimate this ...
ali's user avatar
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1 answer
128 views

CatBoost solves the problem of bias in pointwise gradient estimates

I've been reading the following papers: https://arxiv.org/abs/1810.11363, https://arxiv.org/abs/1706.09516 and https://www.researchgate.net/publication/318030603_Fighting_biases_with_dynamic_boosting. ...
Patricia Brezeanu's user avatar
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1 answer
65 views

Beginner Question on Understanding Linear Classifier

I have been trying to understand the math behind Linear classifier for images and I'm hitting a roadblock to understanding this image below: I can to some extent agree that we stretch the pixels into ...
joesan's user avatar
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1 vote
0 answers
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Data collection after the model is built and deployed

I have built a machine learning model which predicts whether a customer will buy a product or not. The model performs well on cross validation tests. Now, I will deploy it in production to recommend ...
Sanyo Mn's user avatar
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1 vote
0 answers
22 views

Strong bias from Linear SVR meta model

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
Tim Stack's user avatar
  • 121
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1 answer
35 views

How to examine effect of variable not used in training a neural network

I am currently using tensorflow to create a neural network that does basic binary classification, and I would like to examine the bias of the model after training. I have a dataset of about 300,000 ...
RLB's user avatar
  • 23
2 votes
1 answer
131 views

Loss function to prevent estimator bias

I have a regression problem I'm trying to build a model for: Predicting sales per person (>= 0) depending on some variables. I'm running different model types and gave deep neural networks a try. ...
JanS's user avatar
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1 vote
0 answers
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how to test if the target variables is correlated with protected variables?

I wonder how to check if the protected variables in fairness either encoded in the other features (non-protected). Or if they are not sufficiently correlated with target variables so adding them does ...
Saif's user avatar
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1 answer
77 views

How to provide Intentional Bias towards recent examples in Text Classification?

I have trained an XGBClassifier to classify text issues to a rightful assignee (simple 50-way classification). The source from where I am fetching the data also provides a datetime object which gives ...
Himanshu Tanwani's user avatar
1 vote
0 answers
32 views

Do these values of bias and variance make sense?

I have this code: ...
kasofi9051's user avatar
0 votes
1 answer
142 views

Look ahead bias predicting a time series using features

I am making some ML methods (RF, RNN, MLP) to predict a time series value 'y' based on features 'X' and not the time series 'y' itself. My question is regarding the bias I might be including since I ...
JmML's user avatar
  • 3
0 votes
1 answer
1k views

Backpropagation of Bias in Neural networks

My goal is to calculate backpropagation(Especially the backpropagation of the bias). For example, X, W and B are python numpy array, such as [[0,0],[0,1]] , [[5,5,5],[10,10,10]] and [1,2,3] for each. ...
Soon's user avatar
  • 125
2 votes
1 answer
115 views

learning curves of a classification algorithm

I a trying to understand this learning curve of a classification problem. But I am not sure what to infer. I believe that I have overfitting but I cannot sure. Very low training loss that’s very ...
xavi's user avatar
  • 121
0 votes
1 answer
62 views

Data snooping and information leakage?

I need help in deciding whether my below implementation imposes data snooping bias and information leakage from the test/evaluation set to the train set. I have a text corpus of 10k+ short online ...
lazarea's user avatar
  • 299
1 vote
1 answer
132 views

The behavior of the cross validation error and training error in underfitting case is not clear

I currently study the "Machine Learning" course on Coursera.org by Andrew Ng, it comes to a topic that discusses the performance of learning algorithms under different conditions. Here, we ...
Ahmed Hesham's user avatar
1 vote
0 answers
394 views

Keras model prediction always has unwanted offset

I am trying to predict next 10 days by looking into the last 60 days. So tried to implement an LSTM layer. Before jumping into the question, I want to clarify a few points. Firstly, this is a Multiple ...
Alper91's user avatar
  • 61
1 vote
0 answers
43 views

Fractional Differencing/Differentiation for Non-Time based Model; Look-ahead bias?

I have time-series data, but instead of using a time-based model like RNN, I've decided to approach my classification problem using an lgbm classifier. To do so, I have modified the data, such that ...
Michael Mech's user avatar
0 votes
1 answer
41 views

I am not sure whether I am being asked to calculate the variance or the irreducible error

The question: Suppose we randomly sample a training set D from some unknown distribution. For each training set D we sample, we train a regression model to predict $y$ from $x$. We repeat this 10 ...
rachel tan's user avatar
1 vote
1 answer
295 views

Does class weighting encourage overfitting when the true class distribution is imbalanced?

I am working on a classification problem in which ~90% of samples come from class 1 while ~10% of samples come from class 2. I have been using various techniques to combat the class imbalance while ...
tensormoby's user avatar
1 vote
0 answers
38 views

Checking my understanding of the bias-variance tradeoff

Hello fellow DS aficionados, I'm trying to clarify some confusing feedback I got from a homework problem. We were told to recreate the bias-variance tradeoff graph using the first graph below as an ...
FLAN - Legacy's user avatar
2 votes
1 answer
388 views

Explanation of Karpathy tweet about common mistakes. #5: "you didn't use bias=False for your Linear/Conv2d layer when using BatchNorm"

I recently found this twitter thread from Andrej Karpathy. In it he states a few common mistakes during the development of a neural network. you didn't try to overfit a single batch first. you forgot ...
KDecker's user avatar
  • 123
0 votes
1 answer
758 views

Visualizing the equation for separating hyperplane

I was wondering if I can visualize with the example the fact that for all points $x$ on the separating hyperplane, the following equation holds true: $$w^T.x+w_0=0\quad\quad\quad \text{... equation (1)...
Rnj's user avatar
  • 225
0 votes
0 answers
26 views

Is it always that lower tree with higher bias but higher tree with higher varaince

When dealing with bias and variance trade-offs, I always hear that in tree models: shallow tree = high bias but low variance, deep tree = low bias but high variance. Someone may also quote from high ...
rain keyu's user avatar
1 vote
4 answers
885 views

What do "Under fitting" and "Over fitting" really mean? They have never been clearly defined

I am always getting lost when dealing with these terms. Especially being asked questions about the relationship such as underfitting-high bias (low variance) or overfitting-high variance (low bias). ...
rain keyu's user avatar
0 votes
1 answer
57 views

How to introduce bias in a machine learning model?

How can I introduce bias for a decision tree model while building an ML application? e.g. If I am building a stock trading recommendation algorithim, I would want to recommend a stock only when the ...
PyNoob's user avatar
  • 83
3 votes
4 answers
459 views

Difference between ethics and bias in Machine Learning

I'm confused about the difference between "ethics" and "bias" when those concepts are discussed in the context of Machine Learning (ML). In my understanding, ethical issue in ML is ...
Qwerty's user avatar
  • 31
0 votes
3 answers
200 views

Imbalanced classification with bias

The problem: A business historical heuristic rule for offering a special deal to customers has created a bias in the dataset when trying to use machine learning in order to make a more sophisticated ...
lml's user avatar
  • 71
1 vote
0 answers
25 views

Generalization error problem on training set

Training data: $\mathcal {T} =\{(2,1),(3,2),(4,6),(0,0),(1,1)\}$ you already computed a predictor for the output using linear regression by least squares, where you used the first 3 samples as ...
todapod264's user avatar
0 votes
1 answer
42 views

KNN Variance using a high value of K and cross-validation

it has come to my understanding, that a value of K=1, gives a high variance because we are only using only one data point, hence we are very likely to model the noise in that training example. Bias: ...
Chukwudi Ogbonna's user avatar
1 vote
0 answers
31 views

Active learning with mixture model cluster assignments - am I injecting bias here?

Suppose I have a dataset of people's phone numbers and heights, and I'm interested in learning the parameters $p_{girl}$, $p_{boy}=1-p_{girl}$, $\mu_{boy}$, $\mu_{girl}$, and overall $\sigma$ ...
goopy's user avatar
  • 76
3 votes
0 answers
287 views

Unbiasedness of random forests

Suppose that I am trying to build a random forest by subsampling the data and choosing a single feature per tree randomly. For example, suppose there is some dataset, $D = \{(x_{1},y_{1}), ......(x_{N}...
user1234's user avatar
  • 131
1 vote
1 answer
565 views

Mathematical bias and weight vs machine learning bias and weight

I am a little confused about the term Bias and Weight with respect to machine learning. Say we want to predict the heights of people whose weights are given. So plot weights to x-axis and height to ...
Encipher's user avatar
  • 361
0 votes
1 answer
1k views

Neural network: does bias equal to zero, is the same as, a layer without bias?

Question as in the title. Does bias equal to zero, is the same as, removing bias from the layer? Here's a pytorch implementation to showcase what I mean. ...
Kyle_397's user avatar
  • 103
1 vote
2 answers
87 views

Model Selection using Bias Variance Trade Off

I have a Regression Model with Train MAPE as 6% and Test MAPE as 15%. This appears to me as a clear case of over fitting. But can I still use this model assuming 15% Error is not a bad number after-...
Manish's user avatar
  • 11
0 votes
0 answers
38 views

how to know if there is a bias in data collection methods

I am collecting data for machine learning models I want to build for some application. I started with random sampling (just simply collecting 'recent' data) but I am not getting enough records of ...
Hiro Nakagame's user avatar
1 vote
1 answer
34 views

Is there a quantitative way to determine if a class of algorithms tends produce low bias or low variance models?

I understand that some machine learning models tend to be low bias, whereas others tend to be low variance (source). As an example, a linear regression will tend to have low variance error and high ...
Brian_E's user avatar
  • 13