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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How to Predict the sales of all the items, offered in all the countries

I am working on a task to predict the sales of all the items offered in all the countries. The sales are aggregated on a daily and country level. Each Item has a history of past sales and prices for a ...
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Regression with a growing year over year dataset

I created a hierarchical GAM to model final event sales as a function of sales to date, days until event, teams that are playing, and event month with a grouping at the home team level. This yielded ...
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H2o interpretability how to parallelize

I have trained a model using autoML everything is ok. But this model is for healthcare therefore is very important the interpretability so I have used "iml" Package in combination with the ML model ...
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Model recalibration in R for machine learning models [on hold]

I would like to recalibrate my model using R. I tried Frank Harrell's rms package but this one does not allow statistical or machine learning approaches. How can I recalibrate my model using ...
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How did these researchers determine the confidence interval of the AUROC using resampling but without retraining the model?

In this Nature article backed by Google, the investigators develop then externally validate a deep learning model for predicting lung cancer using CT scans. In their internal validation results, we ...
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How to perform regression on data described by a stochastic function?

If I have some data which is described by the following function $ y_n = a_0 + a_1 x_1 + a_2 x_2 $ I can perform a linear regression to extract the values of the coefficients $a_0, a_1 \& a_2$. ...
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How to tune hurdle model with machine learning models?

I am working on predicting count data. Most of the output data is 0. I want to build a hurdle model with the first level being a classification model determining whether the output is 0 or not. And ...
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Deep Learning for small 1-dim Datasets

I am trying to find a neural network architecture for a dataset (150 instances) with 10 features (numerical). The features are not associated to each other, so 1d-convolutions are not an option. ...
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Inverse Hyperbolic Sine Transformation (IHS) for dependent variable - How to back transform predictions?

I am doing IHS transformation for the dependent variable (count data, mostly 0 and small counts) while training a non-parametric tree-based machine learning model. I've seen posts saying it will ...
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Using neural network with mnist cvs data format [on hold]

I've been reading this book about neural networks, called "make your own neural network" and there he tests his neural network with data from MNIST, however the sample he is using is not the same as ...
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Can anyone suggest the source to study basics needed for The Elements of Statistical Learning by Trevor Hastie Robert Tibshirani Jerome Friedman [duplicate]

anyone suggest the relevant study material to get the basics cleared for understanding the book The Elements of Statistical Learning by Trevor Hastie Robert Tibshirani Jerome Friedman
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Retraining only fully connected layer

What kind of features fully connected layer have? For example using transfer learning if we just transfer the fc layer of target model to fc layer of source model and rest of model is assigned random ...
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neural networks and optimization problems in general

Neural networks are efficient at solving optimization problems. The topic of optimization problems is divided into linear and nonlinear problems and in linear and nonlinear conditions. I just wonder ...
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Model for simultaneous person detection and pose estimation

Does someone know a model that performs person detection (eg using a bounding box like YOLO or Mask-RCNN) and simultaneously pose estimation (like CPM or Personlab) in one forward pass. The models I ...
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21 views

How to decode a deep neural network as an analytical expression/formula? [on hold]

The question pretty much says it all. A shallow neural network is simple to do by hand but how to do it for a deep neural network (or is it possible without needing a mile long paper)? This becomes ...
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What is the course structure of mathematics included in AI AND MACHINE LEARNING? [on hold]

I have just finished my basics in python....and automation....and I want to move Forward to AI amd machine learning. I'm a beginner. Please I need some help here!
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Best approach to an event detection classifier model

Probably my title is not as precise as I'd like it to, but bear with me, the problem is quite straightforward: I have daily time-series sales and stock data, from January to May, both in 2018 and 2019....
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Understanding the process of transfer learning for NLP

Full Disclosure: I am a machine learning newbie. I have been learning about natural language processing for the past few weeks. To my understanding, the process of creating a supervised text model ...
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1answer
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Higher RMSE but lower MAE and RMLSE. Which model is better? [duplicate]

I am evaluating two machine learning models. The output is count data which has a range of 0 to 30, which most of the output values being small values. Large output values are rare. One model has ...
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1answer
47 views

How to find similar users in a social network

I have a set of users from a social network. These users are represented by large sparse vectors. Let's say that a small subset of those users bought a ticket for a particular movie. How could I find ...
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Interpreting Local Outlier Factor (LOF) results

Using this example on the scikit-learn site, I am attempting to do some anomaly detection using LOF. What I end up with is this: ...
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1answer
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How to specify and fit a hybrid machine learning - linear model

I want to understand how some dependent variable y, depends on a known relationship with independent variable x, but also how <...
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1answer
18 views

Linear Quadratic Regulator

from http://people.eecs.berkeley.edu/~somil/Papers/lqrlecture.pdf Why must the matrices be positive semidefinite? What is the input authority cost? What is the purpose of multiplying the transpose ...
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Log Likelihood Glmnet

I am not exactly sure if I understood the glmnet algorithm correctly (https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html). It says it uses a maximum likelihood approach to find a solution. ...
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I need to inject a scalar value on my pix2pix model but there is something wrong [on hold]

I am a newcomer in this forum so I don't know if anyone's ever asked that question before. I need help in my work: I want to implement a model that conditions a Generative Adversarial Network with an ...
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How to optimise 3-layered NN for regression predictions?

I'm trying to train a NN model on a regression dataset and trying to predict capacity. The size of dataset is 20773. My model is as: ...
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Is it possible to refit after cross-validation and continue to train the model for better accuracy?

A weird question below. Suppose you did a 10 fold cross-validation to show that a model is an unbiased estimator. And the results of the cross-validation also shows that training the model longer ...
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41 views

Why does feature scaling improves accuracy? [duplicate]

With feature scaling we just change representation of the data. This can make our model run faster but how this can improve accuracy? It is the same data after all. When I train my SVM without ...
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Generate synthetic data given AUC

I'm experimenting with ROC-AUC for binary classification problems. I want to generate synthetic data for a given AUC score. The ...
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Can a binary classification model be used to classify samples on a probability scale?

Currently I have a machine learning model classifying samples into 4 groupings. But I am wondering if it is possible to make this a binary classification problem, using training data with only binary ...
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Suggestion to use transfer learning in detection of Environmental norms violator [on hold]

I am working on one research, where the goal is to predict if a particular company will violate environmental norms in future from their websites screenshots. So, this goes to be binary ...
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Gradient Boosted Decision Trees How to Find Prediction of Each Tree? [on hold]

I'm doing a project. I have a classification problem that I should solve using gradient boosted decision trees. What I want to do is create a matrix that gives prediction of each decision tree for ...
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22 views

In gradient descent, could higher order gradients help to escape non global minima?

Im new to optimization so sorry if this question is ridiculous. In gradient descent/ascent based optimization, one big problem seems to be getting stuck in a local minimum randomly. Ways in which I ...
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How to compare different data preprocessing when using CNN from sratch

Let's says you use a CNN for image classification. You have binary images: pixel values = 0 or 1. Some tools can be used to get those images with continuous values (i'll not explain how since that ...
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Training data for extracted license plates from car images

I am working on a project which uses machine learning and image processing techniques to detect/extract license plates of a vehicle given an image. In my module for data preparation and feature ...
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Intuitive explanation of scaled inverse chi-distribution

I am having a hard time understanding the scaled inverse chi-distribution. I looked for Wiki and other resources which are pretty math heavy without an intuitive ...
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How to use machine learning to create combine of opposite images side by side [on hold]

Inspired by: Two Worlds Pictures I just want to create a Machine Learning Model that can automatically combine the opposite images into 1 image. I am thinking about 2 possible solutions: Pose ...
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22 views

Grouping timeseries to improve the accuracy of prediction [on hold]

I realized when trying to predict a timeserie that the algorithm can benefit from grouping timeseries together. If I'm trying to predict the number of new cars produced for the next month, it seems ...
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Peak detection and smoothing data

I'm working on smoothing data. But I want to retain the peak actual value and draw the actual value along with other smoothed data. And I use the Robust peak detection algorithm but it didn't work the ...
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15 views

Why deep Q learning works?

In DQN we evaluate true Q value by formula $$r + \gamma \max \hat Q\left( {s,a,{w_{{\rm{targe}}t}}} \right)$$ And use the output of Network to fit it. Why we can use this formula to approximate true ...
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Maximization bias in reinforcement learning

In Richard S. Sutton and Andrew G. Barto's book on reinforcement learning on page 156 it says: Maximization bias occurs when estimate the value function while taking max on it (that is what Q ...
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Is each row of latent factors obtained from matrix decomposition (SVD) dependent on the other rows of the higher dimensional matrix?

I implemented a recommendation system using user-user interaction data, learning missing ratings through alternating least squares and matrix factorization, which as I understand it, adjusts and ...
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What does “knn is only approximated **locally**” mean?

Wiki gives this definition of KNN In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input ...
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Predicted individual treatment effect with continous treatments

I'm trying to apply Rubin's counterfactual model in an observational setting using machine learning predictions to simulate the unseen treatment-outcome pairs, according to https://www.ncbi.nlm.nih....
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What is the relation between “conjugate priors” and the approximate inference?

I know that "conjugate prior" is to help us calculate the the denominator of the Bayes formula(to make the calculations easier). And I just learnt to approximate the inference by mean field ...
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How to show an alternate data processing inequality concerning KL divergence between conditionals?

Suppose $(Y,X) \sim F \in \mathcal{P(\mathbb{R^d})}$. Consider an arbitrary transformation $f$ that acts on $X$. My intuition is that the following should be a result in information theory: $$ \...
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what is a parameter and hyperprameter [duplicate]

I hear in many articles the word parameters and hyperparameters but I don't know what they mean by that. Are they variable or the weights of the nodes? explain me in an intuitive way as an analogy ...
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21 views

predicting who will buy what [closed]

what machine learning algorithm would be most suitable to predict which customer will buy what and when they will buy it ( consider historical data is available). I have tried : predicted next ...
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The extrapolation problem: model selection, performance metrics, and improvement

Machine learning models are fit to a response within a given range. This leads to weak and sometimes disastrous performance when it comes to instances outside that range. When the underlying mechanism ...
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what is the cost function for a perceptron muticap

I have the function of cost or error of a perceptron of an entry and exit ...