Questions tagged [bias]
The bias tag has no usage guidance.
101
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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) ...
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LSTM model constantly underpredicts
I have a dataset that consists of temperature measurements. The temperature is increasing as time goes by. I have developed different RNN models (LSTM,BiLSTM,GRU,BiGRU) to predict future values of ...
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bias in lgbm output that grows over number of trees
so i am training an lgbm regressor on targets that have mean zero. i would expect this to yield an average prediction that is also distributed with mean zero. however, i weirdly observe that the ...
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53
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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 ...
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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 ...
- 284
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36
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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 ...
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69
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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. ...
- 179
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Understanding data characteristics for high variance
I understand the interplay between bias and variance. But I am not able to understand what below figure intends to communicate. Can someone please help me understand? ref
The explanation given is:
...
- 205
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L2 and L1 regularization: Why avoiding mutual cancelations of errors cause the skewness (bias) can't be determined?
I'm trying to understand about the L1 and L2 regularization and in this paper (https://arxiv.org/pdf/1809.03006.pdf) the authors mentioned the problem with using the mean absolute error or mean ...
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Relation between bias, variance and model choices
Are non-parametric model more prone to high variance while parametric one to high bias? People say linear regression, logistic are models with high bias while SVM and tree models have high variance. ...
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How can create deliberately biased models?
I deal with an image classification problem with 3-class. I want to create a model which takes side to one specific class. I mean, while the model predicts a sample, if it is hesitant between class-1 ...
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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 ...
- 131
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253
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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 ...
- 125
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87
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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 ...
- 133
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78
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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.
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50
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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 ...
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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 ...
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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% ...
- 121
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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 ...
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55
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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. ...
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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 ...
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51
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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 ...
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29
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66
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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 ...
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739
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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. ...
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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 ...
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54
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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 ...
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98
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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 ...
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255
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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 ...
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29
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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 ...
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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 ...
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182
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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 ...
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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 ...
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189
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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 ...
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511
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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)...
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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 ...
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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).
...
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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 ...
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318
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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 ...
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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 ...
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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 ...
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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: ...
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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 $\sigma$ ...
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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}...
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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 ...
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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.
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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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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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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 ...
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Bias variance tradeoff boosting (xgboost) vs random forest (randomized bagging) which to use when?
I was looking up differences between boosting an bagging and I see this quoted everywhere
If the classifier is unstable (high variance), then we should apply
Bagging. If the classifier is stable and ...
