Questions tagged [bias]

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283 views

Learning curve using micro F-score and macro F-score

I plotted the learning curves using micro and macro F-scores for a Multinomial Naive Bayes classifier. The first plot is made using micro F-score, and the second using macro F-score. I find it quite ...
2
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1answer
29 views

How to measure deviance resulting from different random seeds in machine learning?

I'm running an xgboost model to predict probabilities to a binary classification problem. Then I aggregate the results based on the Age variable (what is the aggregated risk of getting the sickness ...
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0answers
76 views

Bias Formula in Machine Learning expanded using ground truth

Why is Bias calculated for $f(x)$? Shouldn't it be calculated for $Y$ (which is $f(x)$ + Noise $\epsilon$)? We are fitting our model to $Y$, So shouldn't we be calculating bias wrt to $Y$? Also, I ...
2
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1answer
29 views

What is the defining Set in NLP

I am reading the paper Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings here is the pdf. On page 6, we read: ...
2
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0answers
104 views

Math behind, MSE = bias^2 + variance

Based on the deeplearningbook: $$MSE = E[(\theta_m^{-} - \theta)^2]$$ $$equals$$ $$Bias(\theta_m^{-})^2 + Var(\theta_m^{-})$$ where m is the number of samples ...
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0answers
23 views

When should the bias b be updated with weights w and when should it be updated seperately?

It seems in some Machine Learning models, the bias term $b$ is updated just like other weights $w_i, i=1...n$. For example, in Logistic Regression, using SGD, $b \ \text{or} \ w_0$ is updated with: $$...
1
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1answer
32 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-...
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0answers
20 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 ...
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0answers
18 views

Effect of batch during prediction

During prediction (not training), is it normal to get different loss for different batch size? Worst case happens when I use batch_size=1 for test dataset. The prediction performance get pretty bad. ...
1
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0answers
26 views

Do non-parametric models always overfit without regularization?

Let's scope this to just classification. It's clear that if you fully grow out a decision tree with no regularization (e.g. max depth, pruning), it will overfit the training data and get full accuracy ...
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0answers
75 views

Derivative of Loss wrt bias term

I read this and have an ambiguity. I try to understand well how to calculate the derivative of Loss w.r.t to bias. In this question, we have this definition: ...
1
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0answers
283 views

How to calculate irreducible error using Bias and Variance for a given machine learning Model?

I am trying to calculate the Bias and Variance for a ML Model. $$ Err(x)=E[(Y−\hat f(x))^2] \\Err(x)=Bias^2+Variance+Irreducible\ Error $$ $\hat f(x)$ is our model $Y$ is the variable we are trying ...
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0answers
14 views

Measuring the bias of a machine learning model

How can we measure the bias of a machine learning model? Can we determine it by just calculating its performance estimates difference on the train data and test data? For example, if a model SVM ...
1
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0answers
328 views

Calculation of Neural network biases in backpropagation

While learning neural networks I've found a basic Python working example to play with. It has 3 input nodes, 4 nodes in a hidden layer, 1 output node. 5 data sets for training. The initial code is ...
1
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2answers
110 views

bias variance decomposition for classification problem

It is given that: MSE = bias$^2$ + variance I can see the mathematical relationship between MSE, bias, and variance. However, how do we understand the mathematical intuition of bias and variance for ...
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0answers
199 views

Bias of 1 in fully connected layers introduced dying relu problem

While implementing AlexNet (model-code), one of the thing I need to do was to initialize the biases of the convolutional layers and fully connected layers. Normally we initialize biases with 0s, but ...
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0answers
10 views

Dropping Missing Observations under MAR Assumption

Some of the outcome data in my data set are missing. I believe that the missing data mechanism is missing at random (MAR) as the observed characteristics significantly differ between the missing and ...
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0answers
32 views

Odd results on bias-variance tradeoff assessment

I am running a bias-variance tradeoff assessment on ten regression models of increasing complexity (linear to x^10), but my results do not satisfy the MSE = Bias^2 + Variance + True Error relationship....
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19 views

DNN predicting the same value for train+test Data

I have trained a Deep Neural architecture for regression problem and after the hundred's of epochs, model predicting the same output for both training and testing data. When I reduced the batch size, ...
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0answers
16 views

Is recommendable look for high variance when your data is imbalance?

Hello I have a dataset with the following classes A, B, C, and this classes have the following representation of the dataset 60% 39% 1%. Is it a good idea try to get a model with high variance in this ...
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19 views

Question regarding strata in Geron's book

In the book "Hands-On Machine Learning with Scikit-Learn and Tensorflow" by Aurélien Géron. There is a regression project explained. My doubt is regarding his example for 'stratified sampling'. He ...
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30 views

Exploratory statistics, how to idenify and remove driver (bias)

I am looking at customer data, and created frequency tables (+histograms) for customers with different professional statuses and what the best time is to reach them. Status ranges here from employed, ...
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0answers
18 views

How to reconstruct an unbiased data set?

Imagine that every month, I have a "virgin" data set consisting of data points (e.g. people that have stopped paying a subscription) with certain features (e.g. geo-demographic information and payment ...
0
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1answer
33 views

Amount of data needed for deep learning vs support vector machine

I often read about the fact, that the amount of data to train and get a generalizing model for a deep learning algorithm is much higher in comparison, e.g. to a support vector machine. It makes sense, ...
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35 views

I have tried 5 different types of model but all returns really low training accuracy (~64%) and low testing accuracy (~14%). What should I do?

I am working with a typical regressor problem. There are $6$ features in the dataset that I am concerned with. There are about $800$ data points in my dataset. The features and the predicted values ...
0
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2answers
48 views

Bias and variance in the model o in the predictions?

This topic confuses me. In the literature or articles, when talking about bias and variance in automatic learning, specifically in cross-validation, do they refer to the high bias (underfitting) and ...