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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281 views

How does word2vec work for word similarity?

I am trying to apply word2vec/doc2vec to find similar sentences. First consider word2vec for word similarity. What I understand is, CBOW can be used to find most suitable word given a context, whereas ...
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1answer
146 views

What would be the final hypothesis like? if our unknown target is a distribution rather than a function?

The above picture is about building model, it seems a bit difficult to understand the meaning of "plus noise", and what would the final hypothesis look like? if the unknown target changes from ...
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41 views

Bayes formula alternate expression using alpha

I know that Bayes theorem is: Posterior = Likelihood * Prior / Evidence However, I am confused about the above notation in the picture. How do we get to the above three notation? How does ...
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1answer
65 views

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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17 views

Draw chance in games as a range?

I am working on my first project with a lot of data and outcome prediction. One goal is to calculate the expected outcome of a game between two parties. (Parties don't have to be of equal "strength" ...
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4answers
8k views

What is the difference between independent variable and a feature?

I ran into this question which asks the identification of various terms for a linear regression function (f). I am confused about the "independent variable" definition. What is the difference ...
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1answer
16 views

Machine learning on Percent as dependent variable [on hold]

I am working on a problem where I am tasked to predict users into 'High users' and 'Low users'. Dependent variable provided is in percent of orders (%) which is calculated as (#orders placed/#sales ...
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452 views

Machine Learning on Percent/Continous Dependent Variable

I have a large dataset of 30,000 cases with 150 variables. I am looking for a few possible machine learning solutions/methods that I could try and use for cross validation. My dependent variable ...
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1answer
1k views

guide for text classification using weka

I have a set of 2000 small texts (each less than 500 words) that I manually categorized. All the texts are in the same main subject, and I want to separate them into distinct groups based on their ...
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1answer
2k views

Why isn't Akaike information criterion used more in machine learning?

I just ran into "Akaike information criterion", and I noticed this large amount of literature on model selection (also things like BIC seem to exist). Why don't contemporary machine learning methods ...
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2answers
71 views

Are loss functions necessarily additive in observations?

In all of the contexts I've seen loss functions in statistics/machine learning so far, loss functions are additive in observations. i.e.: loss $Q_D$ of dataset $D$ is an additive aggregation of losses ...
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2k views

Use Pearson's correlation coefficient as optimization objective in machine learning

In machine learning (for regression problems), I often see mean-squared-error (MSE) or mean-absolute-error (MAE) being used as the error function to minimize (plus the regularization term). I am ...
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2answers
806 views

Nonlinear Dynamic Online Classification: Looking for an Algorithm

I have two predictors a,b that I want to use combine to classify data. a is stable, it will always produce the same prediction for the same input. b will change and probably improve in time (because ...
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39 views

I want to know the relationship between Discriminant functions and the kernel in SVM

The following articles are reprinte of #3338212 of math.stackexchange.com. It was recommended to ask this community at math.stackexchange.com. The following 【Quiz】 and 【Official Answer】are the ...
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1answer
890 views

How to use weights with Elasticnet regression in python?

I am using Elasticnet from scikit-learn in python, I've also used Glmnet package in R for prototyping. I want to use weights in Elasticnet which apparently is not available as an option/argument in ...
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7 views

How can I detect multiple features from a video stream [closed]

I want to detect multiple things when a person steps into view of the camera. Their face Their badge How would I go about implementing a classifier to detect two objects and put them together into ...
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1answer
56 views

KL Divergence of two standard normal arrays

I generated two 9000,1 np arrays with a = np.random.standard_normal(9000) b = np.random.standard_normal(9000) Then I check the KL Divergence with ...
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1answer
40 views

Should exploratory data analysis include validation set?

I know that EDA should be performed on the training set but not on the test set. But my question is: we usually split the training set into training and validation datasets. Should we perform EDA on ...
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2answers
1k views

Scaling the backward variable in HMM Baum-Welch

I am just trying to implement the scaled Baum-Welch algorithm and I have run into a problem where my backward variables, after scaling, are over the value of 1. Is this normal? After all, ...
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0answers
13 views

regression method to predict differences between A and B given P(A>B) as prior probabilities

First off I apologize if this has been asked before, I tried searching for came up empty handed. Please forgive me if this is duplicated. I have 2 streams of data A and B which are pricing data, each ...
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1answer
635 views

What is an autoregressive decoder?

I saw that this was part of a deep belief network I was looking at. I'm not sure what it means. Is it a layer that transforms few inputs into many outputs and has a connection to itself? What is an ...
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1answer
66 views

How is TD(1) of TD(lambda) equivalent to Monte Carlo?

In Sutton and Barto's book about RL they say that the TD($\lambda$) algorithm is equivalent to Monte Carlo when $\lambda = 1$. I don't see how that is the case. They define the lambda return as: $$...
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1answer
26 views

My approach to deal with the effects of randomness in a ML model

I'm not a statistician, so please excuse a possibly wrong use of terminology here. My dataset has about 400 - 800 samples and about 800 features. The samples are ordered by time, although it is not a ...
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1answer
20 views

Why we fit xᵢ vs errorᵢ in Gradient Boosting

The basic idea of Boosting is to reduce bias by reducing training error in multiple iterations. However, I'm unable to understand how does combining multiple models which are trained by fitting ...
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9 views

EPE for a categorical variable

I am going through "Elements of Statistical Learning", and my memory of statistics is a bit rusty at this point, so I have trouble understanding the following equation from the book: $$EPE = E_X \...
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1answer
28 views

Is increasing the class weight of minority class in Random Forest algorithm decreasing the performance?

I am trying to classify an imbalanced dataset (census dataset with approx. 3:1 imbalance) using Random Forest algorithm in python, and what I observed that increasing the class weight of the minority ...
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21 views

In semantic segmentation using Fully convolutional networks, why is Cross Entropy loss preferred over L1 or L2 losses?

I am training a fully convolutional network with Encoder-Decoder architecture for the task of Image Segmentation and currently am using the Binary Cross Entropy loss for foreground/background ...
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4 views

Exhaustive Bipartition Search (Specific to light GBM or Catboost)

After searching extensively I am unable to find a detail explanation and clear example of how exhaustive bipartition search is used by LightGBM (light gradient boosting) as an 'optimal' solution for ...
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8 views

What is the reason behind the weight updates in Evolution Strategies?

OpenAI introducted Evolution Strategies as an alternative to reinforcement learning technique without backpropagation. A sample code from their website, ...
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1answer
15 views

Alternatives to 1SE Rule for Validation Set Parameter Tuning

I have a general question regarding parameter tuning on a held-out validation set (read: NOT cross validation, but a single held-out set of data). Suppose I would like to tune a parameter in a ...
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1answer
26 views

Learning Deep Generative Models of Graphs

I'm reading through Learning Deep Generative Models of Graphs, which is a paper that seems to me propose some sort of variational autoencoder to generate a graph. At very high level the semantic of ...
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1answer
31 views

Can we actually get the probability of a text using recurrent neural network?

I know that recurrent neural network is used to generate text and to model the probability of $P(x_0,x_1,x_2,x_3)=P(x_0)P(x_1|x_0)P(x_2|x_1,x_0)P(x_3|x_2,x_1,x_0)$ where $x_i$ is words/text. If RNN ...
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0answers
12 views

Creating a regressional model that predicts tha annual awards of a sport league. How to?

I am writing an essay for my high school diploma program and my topic of choice was Machine Learning and NNs. After some thinking I decided to create a model that tries to predict the annual awards ...
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23 views

Why do we report standard deviation instead of standard deviation of the mean in cross-validation?

I finished a statistics course and now I have some doubts about basic procedures used to train machine learning models. For instance, k-fold crossvalidation results are usually reported as: ...
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18 views

How to Create a Toy Example of the Curds and Whey Algorithm?

Why is my simulated example failing? I am trying to create a toy example of the Curds and Whey method for multivariate linear regression in python (An example in R would be very helpful as well). I ...
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1answer
105 views

How is MAP 'not invariant to reparametrization'? [duplicate]

I was watching a lecture on coursera on 'Bayesian Methods on Machine Learning' and I came across a statement that: MAP(Maximum a posteriori) is not invariant to reparametrization. I didn't quite ...
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2answers
59 views

Dependence on k of k-fold cross validation

I struggle to understand k-fold cross validation. I understand it is a tool to check the generalization error of a model and works shuffling the data and diving it into k-chunks. Than $k$ models are ...
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1answer
527 views

Decision tree: where and how to split an attribute on numerical dataset?

I am new to data mining and am manually implementing decision tree classification on a dataset with all continues values. A very small sample dataset of 4 attributes (columns) would be like this: <...
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2answers
48 views

Using k-fold cross validation, I found that Model 1 has MAE lower than Model 2 while MSE is the opposite..which model I should select?

Model 1 : MAE = 2.12, MSE= 8.8, R2=0.89 Model 2: MAE=2.17, MSE=8.408, R2=0.9 Which model will be selected?
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1answer
129 views

What's stopping a gradient from making a probability negative?

Suppose I have a 1000-length vector of zeros and ones and I am modelling each of its components as an i.i.d. Bernoulli. Then the probability of that vector is $$p(x | \theta) =\prod_{d=1}^{1000}\...
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16 views
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27 views

LASSO Regression with noise

I know LASSO regression is useful to exclude redundant features, so can it be useful when you have noisy data? I explain better with this example: Suppose I generated a data set using an equation (e....
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1answer
971 views

MSE as a proxy to Pearson's Correlation in Regression Problems

TL;DR (too long, didn't read): I'm working on a time-series prediction problem, which I formulate as a Regression problem using Deep Learning (keras). I want to optimize for the Pearson correlation ...
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1answer
146 views

How do you calculate precision and recall for multiclass classification with only two classes?

I'm trying to predict the gender of a Twitter account using only the profile information like tweet text, description and used colors. I've trained a SVM classifier and then tested dividing the ...
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1answer
17 views

Cross validation train and test error

I came across this sort of flowchart: Below the flowchart, this is what appears: “Given a training set, cross-validation error is computed for each configuration of tuning parameters (λ,d). The ...
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1answer
14 views

heteroscedasticity evaluation of residuals in linear LASSO regression model [closed]

I plotted residuals for linear LASSO model. Though tests for heteroscedasticity doesn't show any but i am seeing one some lines in residual plots depicting some heteroscedasticity might be present. I ...
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1answer
23 views

Overfitting in recommender systems

So I want to know whether or not my models are overfitting or the difference between train and validation errors are decent. $L$: is the number of neighbors The first column is the train error ...