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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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SVM in the classification layer of a Feedforward neural network

I want to use SVM in the classification layer of a 2 layer feedforward neural network. Need guidance from the community on how to approach this problem. This involves capturing the features from the ...
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1answer
538 views

Is it possible to use LASSO regression with multi-levlel data?

I have real-time monitoring data where participants report on a variety of variables four times per day for a month. Is it possible to use LASSO regression (e.g,. glmnet r package) with this data? I'm ...
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24 views

Unrelated features with common target issue

The problem is we have many unrelated dimensions with common target value. .... ..... We want to build a formula that predicts this target variable (continuous numeric): Target = Big_Model(Model1(...
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1answer
9 views

How to interpret a high sensitivity and low specificity using svm classifier?

I want to have your opinion on the performance of my SVM classifier (k-fold cross validated): Classification of class1(n=45)/class2(n=86) Accuracy: 65.4% Sensitivity: 88.2% Specificity: 22.2% AUC:...
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Scoring the difference between a family of distributions and a test distribution

Let's suppose we have a random model that I can sample to generate distributions of a certain 1D variable. I want to score the distance of a test distribution to the model in question. The distance ...
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3answers
4k views

Function with multiple local minima

I am trying to understand gradient descent algorithm by plotting the error vs value of parameters in the function. What would be an example of a simple function of the form y = f(x) with just just one ...
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1answer
16 views

Machine Learning problem - label over multiple lines

Currently I'm trying to work out a project where I would like to recognize movements from videos using machine learning and python. What I've done so far is extracting the x and y values of body ...
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Why using RMSE as loss function in logistic regression takes non convex form but doesn't in linear regression?

I am taking this deep learning course from Andrew NG. In the 3rd lecture of 2nd week of the first course, he mentions that we can use RMSE for logistic regression as well but it will take a nonconvex ...
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Looking for ways to transform time-series data recorded from object movement into equation describing the movement direction of the object

Looking for some time-series data transformation advice! I want to know what's the best way to transform data of 9-tuples time series data of IMU (Inertia Measurement Unit) sensor, recorded from a ...
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0answers
11 views

How to add regularization term lambda? [on hold]

I have this equation in online-sequential extreme learning machine. how can include a multiplication regularization term lambda to govern how rapidly the network weights update please any help. ...
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1answer
2k views

SMOTE using unbalanced package in R fails on simple simulated data

SMOTE is a popular method to generate synthetic examples of the minority class in an unbalanced-class data set. I am trying out SMOTE in the "unbalanced" package in R. I am generating a simple ...
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Implement the probability distribution function of a dataset and calculate the Mahalanobis distance of any given new instance [duplicate]

I'm looking for some practical guidance, as I have a limited time to go from theory to implementation. I would appreciate concrete recommendation on libraries, tutorials or similar to implemente a ...
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5 views

Method to identify recurring patterns (motifs) in a time series after giving a reference pattern

I would like to know if there exists an algorithm using which I would be able to extract repeating patterns from a time series dataset, provided I give it a reference shape. I have included an image ...
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synthesized data to train classifier

Our dataset is relatively small (303 x 14) and so we decided to use synthpop package in R. The basic idea of synthetic data is to replace some or all of the observed values by sampling from ...
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0answers
10 views

Random effects model vs Region by region model - which is more accurate for translating to business action?

I have 100 regions and several features like [quality_rating, the density of stores in a region, the number of people who purchase, age, gender, income, lifestyle], etc. for each region. Let's say my ...
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1answer
62 views

Features that are important according to random forest are not significant when logit model was used. How to interpret?

I have a feature set for each customer [age, gender, income, lifestyle, & so on...] and a response variable say: has_repurchased. I use a logit model summary which shows income & gender to ...
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0answers
6 views

Analysis on cluster change

I have 50 datasets each for every year from 1961 to 2010. These datasets keep data about GDP, mortality, natality, etc. My intent is to apply clustering for each dataset and then compare clusters. ...
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What is the scale of pseudo R squared obtained from my Logit model?

I am getting a pseudo R squared in the range of 0.01 - 0.05 when I experiment with various combination of features. I am aware of this post: McFadden's Pseudo-R2 Interpretation says 0.2-0.4 ...
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0answers
26 views

How to derive a ranking function from observations

I have a employee dataset with the following 5 details. projects completed customer ratings number of bugs reported customer complaints profit I want to rank ...
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1answer
17 views

Including the outcome variable in multiple imputation

I'm trying to perform binary classification on a dataset with missing values. I used sklearn's iterative imputer to impute these values and I got pretty good results. However, I realized that I was ...
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0answers
24 views

Can bagging outperform a random forest?

I want to predict wages and constructed bagged regression tree's and a random forest of regression Tree's. The bagged regression tree's outperform the random forest. Is this result even possible , I ...
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14 views

Machine learning for discrete probability distributions when events can be rare

I am following a paper which suggests using a restricted Boltzmann machine for learning a discrete probability distribution. I have encountered a problem, however, when the scientifically "interesting"...
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0answers
7 views

Oversampling in Uplift Modelling

I hope any of you can help me in the following matter: I am about to write my master thesis addressing the question how response and uplift modelling differ in terms of performance but also the ...
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0answers
30 views

Asymmetric or unequal misclassification costs in random forest

I have a general question about asymmetric costs. In machine learning problems, there are times when the cost of a false positive is different from the cost of a false negative. Accordingly, models ...
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1answer
344 views

On the convergence of Iterated Conditional Modes (ICM) for MAP inference

ICM is very fast but I could not find any references that contain a detailed analysis on its convergence (e.g. rate of convergence). Any suggestions please? Thanks a lot for your help!
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19 views

Proof for asymptotic error in logistic regression

Ng, A.Y., and Jordan, M.I. (2001). On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes. Advances in Neural Information Processing Systems, 14, pp. 841-8, ...
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Using sigmoid in binary DNN output layer instead of softmax?

For a binary DNN, the output is $y_0 + y_1 = 1$ since they are the probability distribution, hence the sum must equate to 1. However, I've been told that $y_1$ is sufficient to represent the output of ...
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1answer
11 views

How to interpret precision and recall for multiclass prediction?

I have a few models doing prediction with 4 classes, with the output precision and recall varying with different labels. For example I have (with the class labels being 0, 1, 2, 3 on the x axis): I ...
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0answers
16 views

What are other nonlinear transformation methods in machine learning except Neural Network activation functions?

One advantage of the MLP neural networks is the nonlinear transformation used on raw features. The popular ones used are the activation functions like Sigmoid, Tanh, ReLU, Leaky ReLU, etc. They are of ...
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8 views

How to calculate accuracy of model without having actual labels [on hold]

i have a dataset having traing.csv and testing.csv files. i have trained the model on traing data and then predict the labels for testing data. as the labels are only given in training data. there are ...
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0answers
15 views

Unsupervised Learning: Distinguishing 10 spoken numbers in Arabic

I want to develop an unsupervised learning method to identify spoken numbers in Arabic. My dataset consists of MFCC arrays. Every row consists of an array of shape(41,13), The row consists of float ...
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0answers
14 views

On Structures in Dataset [on hold]

I am working on a dataset challenge and am being asked to detect structures in the dataset. What are some ways we can define structures within the data? Is that pretty much any patterns found? Maybe ...
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1answer
182 views

How do I optimize decision tree regression algorithm implemented in R?

I'm only getting an accuracy of 59% using the following implementation calculated using the diag(sum(cm)) and sum(cm) functions. ...
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0answers
19 views

Are feature importances from tree based models directly actionable for business?

If my response variable say is "has_repurchased" [0 or 1] and I have all customer level features. Can I rank the features in order of importance from the random forest model and report them as whats ...
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11 views

Modeling both linear and non-linear relationship

Apology for being verbose and all the typos or mistakes. This problem has been bothering me for a while and I really hope you can help me with it. Let's say I want to model quarterly sales for a ...
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How does regularized regression overcome the p > n problem?

So, I understand why simple linear or logistic regression will have infinite solutions in this case (good answers here and here). But while LASSO will only select n features, Elastic net does not have ...
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2answers
18 views

Question from Machine Learning Textbook on Number of Models

In the James, Witten, et.al Statistical Learning textbook, it says the following: "Unfortunately, there are a total of $2^p$ models that contain subsets of $p$ variables." Can someone please ...
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1answer
378 views

What is the difference between accuracy and AUC score which one to trust?

I've 300 samples with multiclass classification problem with 3-classes. I implemented SVM in R programming. Below is the output which I am really confused. Can anyone logically explain to me what is ...
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1answer
764 views

Hoeffding's Inequality

I am studying the feasibility of learning from the book Learning from Data. The author uses a bin analogy to discuss the feasibility of learning in a probabilistic sense. I have certain questions to ...
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High accuracy on both training and validation but very low on test set

My CNN model has about 96~97% accuracy on both training and validation sets. But when submitting the test set it got only 24% accuracy. Here's my model: ...
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4answers
28k views

How does rectilinear activation function solve the vanishing gradient problem in neural networks?

I found rectified linear unit (ReLU) praised at several places as a solution to the vanishing gradient problem for neural networks. That is, one uses max(0,x) as activation function. When the ...
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5answers
8k views

Why f beta score define beta like that?

This is the F beta score: $$F_\beta = (1 + \beta^2) \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}}{(\beta^2 \cdot \mathrm{precision}) + \mathrm{recall}}$$ The Wikipedia article states that $F_\...
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1answer
29 views

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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1answer
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1answer
332 views

Unsupervised pre-training for Reinforcement Learning

Since the advent of many unsupervised learning methods, as a pretraining step for the main supervised task (mostly under the name of Deep Learning), it shouldn't be strange to ask, what is the current ...
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0answers
24 views

Recognizing whether a written and spoken number is the same

For our ML assignment we have three datasets. The challenge is about checking whether a written and spoken number refer to the same number. We're using the MNIST dataset with handwritten numbers, and ...
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1answer
4k views

LSTM cell output activation for series

This question is asked from the perspective of finding out if there's a more efficient way for an LSTM to act more as a regression entity rather than just assigning only probabilities to the next ...
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2answers
396 views

Proof of correctness of normal equation

I was taking an online course and saw linear regression being by gradient descent The intuition behind why the method would work seemed plausible. I tried understanding normal equation as to why ...
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1answer
448 views

Influence of word counts from DTM on LDA with Gibbs Sampling

I'm trying to wrap my head around Topic Modeling based on LDA with Gibbs sampling (Griffiths, Steyvers 2004: Finding Scientific Topics). What struck me when reading some Python implementations like ...