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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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What units is my mean squared error if I center and scale my training data?

I have a KNN model that I used to predict the close price on houses. ...
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Inputting playing card values to aneural network

I am trying to create a NN to play a card game wherein each state is represented by the hands of 4 players. Every round, the hand of each player is decreased by 1 (discarded). Each player starts with ...
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Multivriate Time Series Model - ARIMAX

I have Weekly Units sales data of products for 2 years (104 weeks). And I am trying to forecast the Unit sales for each productid for next 8 weeks.. Please find the data image below. note: Productid ...
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Estimating a changing transit time between inputs and output

I work with a chemical process in which there is a time lag between the inputs (raw material quality and cooking parameters) and the output (final product quality). The problem is that the time lag ...
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What is the relation between a loss function and an energy function?

A loss function is a function that measures the distance between the expected value and the actual value of a model (an example of a loss function is the cross entropy). An energy function can be ...
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Calculating a continuous variable with regression trees

I have sample records with several attributes (predictors) and a predicted variable Yes/No. What I need is, given new data that omits the column Yes/No, to know what is the probability of Yes. Note ...
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Training Perceptrons with Backprop

Is it possible to train a simple perceptron with a threshold activation function such as this one: https://en.wikipedia.org/wiki/Perceptron with Backpropagation instead of the perceptron rule? is it ...
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How to generate a project plan template from a list of project plans?

Consider the data for 3 users from the same domain: “Design the UI”, “Develop/code the UI” and “Discuss changes with the client” are the most common tasks. The duration could be a simple average. So,...
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Why we use calibration of Machine Learning models?

From the different websites I got to know how to perform calibration of models. But can anyone tell me the reason behind performing calibration of machine learning models?
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Why test and training dataset should have same number of columns(variables)?

Why can't i train the model with specific number of varaibles and test it with more or less variables. (i know i will get error when i do this). But what is the resaon behind this? The main concept ...
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Do unbiased regression coefficents yield better prediction?

I ask myself if a have a omitted variables bias in my regression modell the coefficients of the model are biased so the mse growth because this coefficents are biased right? So does it mean if i ...
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Datasets for Document Classification problem [on hold]

I am doing a project to make a application that can take pdf and docx documents as input and classify them into various categories such as - Financial - Government and Political - Sports and ...
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KNN works in `class` but not `caret` (Too many ties) [on hold]

I am making a KNN algorithm to predict close_price with about 80,000 rows of this data. ...
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What does the intercept represent in a model matrix?

I am making a KNN algorithm to predict close_price with about 80,000 rows of this data. ...
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20 views

relation among loss function / MLE / Bayesian estimation

I have read a lot of stuff on the relation between minimizing a loss function / maximizing the likelihood / choose a centrality measure of the posterior (Bayesian estimation); but I cannot see a clear ...
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Haul's Correlation-based Feature Selection (CFS) formula spread

I want to use the Correlation-based Feature Selection (CFS) proposed by Haul. I found this formula where $r_{zc}$ is the correlation between the summed components and the outside variable, $k$ is ...
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21 views

Is Stochastic Gradient Descent sensitive to training permutation?

I've recently read that SGD (Stochastic Gradient Descent) is one of the most popular techniques for training Machine Learning algorithms, including DNNs (deep neural networks). However, my ...
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What is parameter chaining in CNN?

I have read an article about the benefits of CNN. One of the points was "parameter chaining". What does it mean? And how does it make CNN more convenient?
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What are general practises used to divide the data into training / dev and test set?

Example: I have am building a dog vs cat classifier and I have collected data from 15 countries. Europe: 1. UK 2. France 3. Germany 4. Italy 5. Finland Asia: 1. India 2. China 3. Japan 4. Russia 5. ...
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How can I visualize and correctly interpret the multi-output of a random forest regression?

I have multiple inputs and multiple outputs for a model I am trying to build using SkiLearn's Random Forest Regression in Python. I have imputed the missing data, divided the columns where the Ys ...
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What to choose?ML project or an Internship? [on hold]

This is slightly off topic but pretty serious for me. I am an undergraduate student in CSE, 3rd year. I am confused in whether to do internship or to make my own project in Machine Learning in my ...
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Manifold in the context of machine learning

In the Deep Learning book by Goodfellow et al. on page 158 it is stated: In the context of machine learning, we allow the dimensionality of the manifold to vary from one point to another. This ...
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1answer
19 views

Practical interpretation of Precision-Recall AUC

I have a classifier with an AUC (PR) of 0.06 which I will use for a practical interpretation. My test set consists of three months of data with a total of 2,200,000 observations of which 0.03 are ...
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1answer
27 views

Predict song genre using LSTM

I have a dataset of songs based on genres. For example, a song may hold {5, 2, 3} as scores set for Sentimental, Rock and Jazz. In total there are 800 songs sequentially arranged. I want to predict ...
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What is the relationship between stochastic mirror descent and stochastic gradient descent?

I don't know much about stochastic mirror descent and was wondering if someone could briefly summarize it in general terms and compare/contrast it to stochastic gradient descent. When I understand ...
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1answer
24 views

Determine if groups have significantly different metric values

I am familiar with using ANOVA to analyze differences among group means. However, I am interested in analyzing differences for metrics other than the mean. Is anyone aware of a statistical test that ...
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18 views

Partial Collinearity in Regression

I had a doubt about the effect of multi-colinearity in regression analysis. I understand if two variables are co-related we cannot disentangle the effects of one from the other on the target variable ...
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1answer
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Setting bias of output layer for imbalanced datasets

From a blog post from Andrej Karpathy on training neural networks: Initialize the final layer weights correctly. E.g. if you are regressing some values that have a mean of 50 then initialize the ...
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how to take a subset of a dataset to fine-tune a neural network?

I would like to build a classifier with 80 000 images and 45 classes. As each epochs takes 1 hour to train, Is there a way to win time by training only a subset of the dataset without lowering the ...
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Probability of Incurring Maximum Loss

In online classification one can use mistake bound learning, where one assumes that all $y$ are generated by some target mapping $h^*: \mathcal{X} \rightarrow \mathcal{Y},\,\, h^* \in \mathcal{H}$. ...
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Calculating the number of neurons and the number of hidden layers for a neural network MATHEMATICALLY

I have a fair idea that a lot of research has been done and is still underway to explore the science behind the black art of a neural network (NN) architecture, i.e., accurately calculating the number ...
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using latent dirichlet allocation to reduce the number of dimensions in bag of words model?

Does anyone have experience reducing the dimensions in a traditional bag of words model? For example, if you want to train a decision tree on a large set of reviews, the size of the vocabulary ...
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2answers
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how to analyze time series data and mark if single data is seasonal or not seasonal

I have data set as shown. It is daily sales data for 4 different product for almost a year. I aggregated the sales of product for each day into . I plotted sales of 4 product as per date and got this ...
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23 views

Gaussian process and its limitations

I once saw the following statement on Gaussian process, ...
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1answer
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What is the relation between a surrogate function and an acquisition function?

A surrogate function is a simpler function than the objective function to evaluate. An acquisition function is used to propose sampling points. In the context of Bayesian optimisation and Gaussian ...
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When is the sum of model predictions the prediction of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...
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2answers
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Correct way of getting generalizaton performance of a model using the whole dataset

Standard practice is to split data into a train/test set, then use the train set for hyperparameter tuning / model selection, using for example cross-validation over the whole training set. Finally, ...
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How to solve MAP problem with images (EHT Bouman's Paper)

I'm not familiar with deep learning. Only know some basic concept about Neural Network. Recently I've tried to figure out the algorithm used to restore Black Hole image. After lot's of searching, I ...
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Methods to predict a binary outcome with ordinal covariates

Just a very general question: what are the most efficient methods to predict one single binary variable $Y$ using a set of ordinal covariates? In my precise case, I have around twenty ordinal ...
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How to determine number of profile in a dataset derivated from repeated measures?

I'm currently working on datasets which have been derivated from repeated measures over time (blood concentrations). Actually the descriptors of these datasets are descripting the shape of the curves (...
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overfiting validation set increase model performance

I'm using CNN for image classification on an unbalance data set. (e.g class A = 1000 image, class C=50 image). I got 16 class. I'm using class weights and in total i have less than 3500 images. I do ...
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Bayesian Linear Regression to Gaussian Process

I'm trying to understand how a Gaussian Process with a squared exponential covariance function can be obtained from Bayesian Linear Regression with a Gaussian prior $N(0,\sigma_p^2 I)$ on the ...
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How to create a heirachical classification using hash table in python? [on hold]

I have tried to implement a classification just like amazon for another taxonomy. but i dont know how it is done. my question is about how to create a hash table in python Example : ...
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1answer
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if different methods have difference 0.5 in third decimal place while finding accuracy then what to conclude?

For the comparison of different feature selection methods and evaluating performance metrics. After evaluation when their is only 0.5 difference in third decimal place. Is this difference is ...
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What is a sparse Gaussian process?

In the paper Junction Tree Variational Autoencoder for Molecular Graph Generation, section 3.2, the authors state that they train a sparse Gaussian process to predict a chemical property, $y(m)$, of a ...
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1answer
25 views

How to use features in regression model with 2 of them in linear relation with the value to be predicted?

I am relative newbie to data science so please excuse me if its a trivial question. I have 6 features and want to predict the 'y'. These features are related to y in the training data-set as follows; (...
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37 views

Machine (supervised) learning and board game [on hold]

Let's assume there's a board game like chess or go (in other words, perfect information game). We don't know how to write a good evaluation function for this type of board games, but we have a lot of ...
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1answer
47 views

VAE: why we do not sample again after decoding and before reconstruction loss?

In many of the VAE schematics and in the original paper, a sampling step is present after decoding and before the reconstruction loss as shown in the image below. The image comes from Stanford CS321n. ...
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Is pooling acceptable to evaluate information extraction?

When dealing with information extraction of unbalanced classes (e.g. the desired class has a prevalence of 0.5%), the required sample size for validation might be huge (thousands of cases and more), ...