Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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0answers
11 views

ANN Train and Test set accuracy becomes 1.0 after second Epoch Keras Classifier

I have the following classification model built: ...
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1answer
18 views

Are skewed distributions problematic to binary classification models?

I am trying to build a binary classification model and while trying to build the model, I have done some research about skewed distributions. I learnt that skewed models should be normalized to be ...
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1answer
209 views

Penalized multinomial regression with missing values

I would like to fit some models on a dataset where I have a lot of missing values. I am especially interested in comparing models fit with and without imputed values, because the dataset has so many ...
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2answers
1k views

Intuition behind perceptron algorithm with offset

I was looking for an intuition for the perceptron algorithm with offset rule, why the update rule is as follows: cycle through all points until convergence: $\textbf{if }\, y^{(t)} \neq \theta^{T}x^{(...
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1answer
17 views

How can a neural network predict a policy vector of non-constant length?

I am reviewing how the AlphaZero works. In the article it is said that the neural network predicts a single value number $v$ and a policy vector $\pi$: ...
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0answers
26 views

How to simply understand gradient boosting on ranking problem?

I am reading Chris Burge's paper about LambdaRank, LambdaMART for learning to rank. We only need to compute the lambda, which is relevant to gradients, and use it to update model parameters, no need ...
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18 views

'False Positive' detection in corona testing using ML techniques

I am working on the detection of false positive test result in COVID-19 testing. The false positive sample are mainly due to the contamination during the routine testing. Samples are handled in batch ...
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0answers
6 views

Reduced Boolean variable dataset by using PCA [closed]

I am Trying to Reduce dataset by using principle component analysis but i have not idea how to done this any help regarding this will be help full
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0answers
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Why identity mapping is so hard for deeper neural network as suggested by Resnet paper?

In resnet paper they said that a deeper network should not produce more error than its shallow counterpart since it can learn the identity map for the extra added layer. But empirical result shown ...
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0answers
7 views

K-fold Cross validation in Transformer-like model with epoch

I'm new to Transformer and am building a Transformer model for classification. Before I added the k-fold validation, I needed 3 epochs to train the model. Here comes the question: say, if I use 5-fold ...
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0answers
14 views

Methods for incorporating a feature in a predictive model that is identical for many (but not all) instances

I have a large dataset of people, with 30 categorical and continuous characteristics (e.g. age, gender, etc). They also have a binary status given to them each year, for example saying if they bought ...
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1answer
197 views

Dimension in Locally Linear Embedding (LLE)

I am using LLE to do nonlinear dimensionality reduction. In my understanding, in the step 3, the eigendecomposition problem is with respect to the matrix M which has the dimension NxN (N is the number ...
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0answers
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Citizens crowding in a city dataset [closed]

I am building a demo dashboard for an app that tracks the location of everyone's phones in anonymized manner to evaluate traffic, crowding dynamics and so one. For my demo I would want to have a ...
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Sampling distribution of loss function [closed]

So the sampling distribution of the likelihood function is a basic idea in frequentist statistics. After asking around, some examples include The score gradient, $\nabla_x \log P(x; \theta)$ tells us ...
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1answer
47 views

Bayesian Prediction Simple Explanation

I have read through other questions on the site and I feel that none provide a great answer to this question. Simply put - could anyone explain, common approaches for generating predictions on new ...
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2answers
139 views

complex models have low bias and high variance

I have trouble understanding the trade-off between bias and variance. I can comprehend that complex models are better able to approach the "true distribution". Therefore, they have low bias. ...
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1answer
63 views

Use a multilevel logistic regression and cross validation

I want to use a multilevel logistic regression for a double purpose, estimating the value of coefficients to explain a phenomenon. At the same time, I want to split the data through cross-validation ...
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2answers
1k views

Is there a method for constructing decision trees that takes account of structured/hierarchical/multilevel predictors?

Is there a method for constructing decision trees that takes account of structured/hierarchical/multilevel predictors, that would allow me to impose domain knowledge or constraints on interactions for ...
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7answers
12k views

How to deal with hierarchical / nested data in machine learning

I'll explain my problem with an example. Suppose you want to predict the income of an individual given some attributes: {Age, Gender, Country, Region, City}. You have a training dataset like so <...
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2answers
3k views

Two worlds collide: Using ML for complex survey data

I am struck with seemingly easy problem, but I haven't found a suitable solution for several weeks now. I have quite a lot of poll/survey data (tens of thousands of respondents, say 50k per dataset), ...
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1answer
32 views

How important is outcome variable scaling in SVM regression?

Should I scale outcome variable for SVM regression? What is the magnitude of impact of outcome variable scaling in SVM regression?
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0answers
11 views

Handling time data

I'm trying to solve a classification problem that has records of customers of a company seeking some service. It has different attributes and one of the attributes is the time the customer has been ...
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1answer
26 views

Is comparing the errors of k-fold cross validations meaningful if the data was not standardized?

in psychological studies, you measure data from different participants. In order to be able to compare the responses between participants you standardize the data. The other day I was talking to a ...
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0answers
16 views

Bayesian networks [closed]

I want anyone who can help me developing a model of traffic safety evaluation using bayesian networks( this method is more advanced statistics)? I have a the 385th questionnaires which it will be ...
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1answer
210 views

Can target encoding be performed on a multi-label classification problem?

Is there a way to perform target encoding on multi label (closed set) problems, obviously target encoding is used on multi-class problems all the time, but i'm wondering if it works for multi label ...
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2answers
456 views

Why do transformers use layer norm instead of batch norm?

Both batch norm and layer norm are common normalization techniques for neural network training. I am wondering why transformers primarily use layer norm.
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1answer
15 views

Breaking substitution cipher with language model

Frequency analysis is a common tool used to break substitution ciphers, but often relies on intuition and guesswork of a human. Since language models can objectively calculate perplexity (how ...
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1answer
345 views

Equivalent Kernel - Bishop Chapter 3

I've been struggling to understand the Equivalent Kernel in Bishop's Pattern Recognition and Machine Learning book. Can somebody explain the following Figure (3.10) from chapter 3.3.3? (image taken ...
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0answers
18 views

The variance of the hypothetical parameters decrease as sample size increases [closed]

I saw this statement in Stanford CS229, in which the instructor said something like $var[\theta]$ goes to 0 as the sample size m goes to 0. Also, I think this $\theta$ is produced after each round of ...
2
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1answer
39 views

Efficient way to use Repeated K-fold cross-validation along with grid search using sklearn

I am intended to know the impact of outlier analysis on SVR's performance. So, I need to have two versions of SVR model: Version_1 with all the original dataset, Version_2 with just the non-outlier ...
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0answers
10 views

I have a set of variables that are correlated to the magnitude of my target variable but not with respect to the direction

If I am expecting to predict returns by using say for instance average price volatility and volume in a time window, then these variables would not help predict the direction but would help improve ...
1
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1answer
168 views

Retraining model in scikit learn Random Forest

I have a machine learning Random Forest model that predicts a certain variable. It's implemented with scikit learn and it works fine. Now, assuming that the prediction relates to month 1, I need a ...
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0answers
10 views

Measuring uncertainty of model prediction by repeat measurements

Say I’ve trained some single value regression ML model (a neural network or something). I have trained this ML model with simulation data. I see that this neural network is good at predicting data in ...
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6answers
27k views

Practical hyperparameter optimization: Random vs. grid search

I'm currently going through Bengio's and Bergstra's Random Search for Hyper-Parameter Optimization [1] where the authors claim random search is more efficient than grid search in achieving ...
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0answers
12 views

What is the best reinforcement learning algorithm for continuous action spaces?

Is there a general consensus on whether we should be using TRPO, PPO, ACER, A3C etc. or is it highly dependent on the situation? It seems ACER and PPO are the current leaders but I'm not sure if this ...
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1answer
43 views

Significant Difference in prediction when using library and coding from scratch in Multiple Linear Regression [duplicate]

I have been trying to implement multiple linear regression from scratch after implementing it using sklearn. The values predicted using sklearn is very accurate whereas the values predicted by the ...
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0answers
8 views

where to find pretrained model for emotion recognition in videos

I am a newbie in machine learning and looking to classify facial emotions from video frames in python. And looking for some pretrained models that could help predict emotions. I am not sure how to do ...
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1answer
225 views

How to design a Neural Network model that combines components of Feedforward and Recurrent features?

I want to design an end-to-end system that has components of both feedforward neural networks and recurrent neural networks. For example the data can have different components (some sequential in ...
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0answers
974 views

Should One Hot Encoding or Dummy Variables Be Used With Ridge Regression?

For a regression problem in which the predictor is a single categorical variable with $q$ categories, Ridge regression can be considered the Best Linear Unbiased Predictor (BLUP) for the mixed model $$...
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0answers
25 views

Re-training regression model on covid data [closed]

I am trying to re-train a regression model (XGB regressor) which was used in the pre-covid times (Feb 2020). The dependent variable for the model is the number of bookings done, and due to covid, the ...
2
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1answer
283 views

How to check cross validation scores for market basket analysis?

If I have a large set of transactions where in each I buy a set of goods and I want to do market basket analysis using either A-priori or FP Growth or any other data mining method, you typically get ...
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1answer
24 views

Computing sample variance

Anyone could help with this question on my homework? I managed to substitute f into the expression, and express the denominator as a summation of kernels. However, I am not sure what should I do with ...
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0answers
9 views

Gait Data Analysis assistance [closed]

I am conducting an experiment for my University Project using the Gyroscope sensor on a smartwatch to analyse 4 individuals Gait Data and create a model to recognise new data inputted. I have obtained ...
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6answers
2k views

When we plot data and then use nonlinear transformations in a regression model are we data-snooping?

I've been reading up on data snooping, and how it can mean the in-sample error does not provide a good approximation of the out-of-sample error. Suppose we are given a data set $(x_1,y_1),(x_2,y_2),......
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0answers
30 views

ProPublica's COMPAS Data | Feature Descriptions [closed]

ProPublica kindly provided the COMPAS Recidivism data in the github: https://github.com/propublica/compas-analysis/blob/master/compas-scores-two-years-violent.csv Most of the features are clear from ...
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0answers
14 views

Do people use “textbook backpropagation” when training neural network?

I know two references on the inner details of backpropagation, Chapter 13, an Introduction to Optimization, 4th edition, Edwin Chong and Stanislaw Zak, 2013 Chapter 7, Learning from Data by Abu ...
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0answers
11 views

Need of Exp bijector in the learning the Normal?

I am trying to understand TransformedVariable class in tensorflow probability. The website provides the following example: ...
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1answer
123 views

Bayes net probability question

I've made this Bayes net based on a problem and I'm trying to find the probability of W but I'm stuck. I know I probably have to use Bayes theorem backwards through to find $P(W)$, but I'm not sure ...
4
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4answers
170 views

Is it possible to use generated non-normal errors with a linear regression model

I am working on linear regression models including classic and robust linear regression models. By classic models, I mean ordinary least square and least absolute regression. Also, by robust models, I ...
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0answers
8 views

Is the adjusted R square good for the interpretation prediction accuracy of machine learning regression model and how to interpret it?

Is the adjusted R square good for the interpretation of machine learning regression (SVM, Random Forest, Elastic Net, etc) model prediction accuracy? How to interpret machine learning regression ...

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