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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34 views

What to do when a neural network cannot overfit one training sample? [closed]

Other questions have addressed what to do when a network does not reach good performance on a (medium / big) training set or that overfitting one training sample requires enough capacity. However, ...
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12 views

Best approach for clustering customer support requests (sentence form)?

I have a million records of customer support requests in sentence form. Something like this: ...
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1answer
31 views

How to identify distinct classes for classification problems?

I'm working with a dataset in which we've taken audio recordings of coral reef habitat from 3 different types: healthy, degraded and restored. From each recordings I have 13 different continous ...
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7 views

What should I do when overfitting appears on AUC and not AUPR

I am training a classifier with imbalanced classes (pos/neg = 0.0006). When training the algorithm (xgboost in this case) I noticed that it shows an overfitting when we look at the AUC but the other ...
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12 views

Using pretrained LSTM and Bert Models in CPU Only Environment - How to speed up Predictions?

I have trained two text classification models using GPU on Azure. The models are the following Bert (ktrain) Lstm Word2Vec (tensorflow) Exaples of the code can be found here: NLP I saved the models ...
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5 views

Multiple Seasonal Trend Decomposition

I have been reading work on Time-Series Data. It seems that multiple seasonality makes life really hard for data scientists. I was wondering if anyone could point me towards some reading or explain ...
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22 views

Why scaling down the parameter many times during training will help the learning speed be the same for all weights in Progressive GAN?

The title is one of the special things in Progressive GAN, a paper of the NVIDIA team. By using this method, they introduced that Our approach ensures that the dynamic range, and thus the learning ...
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10 views

KNN - Distance measure and interpretation [duplicate]

KNN can uses different measure to calculate distance: Euclidean, Manhattan, Hemming and Minkowski. Text available over the net doesn't discuss pros and cons for each of distance measure and when to ...
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9 views

latency not decreased tf-lite post training quantization

I am using efficient-net to classify images. I have trained model successfully and wanted to quantize it using tf-lite. I tried all the methods available in tf-lite quantization to check accuracy, ...
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10 views

Question about true risk and empirical risk of sample S, when both are hypothesised by ERM of S

I was reading through some notes online, and I came upon a property that I don't know how to prove. This is the property: E [ R(ERM(D)) - R'(ERM(D), D) ] >= 0 where D is a sample, ERM(D) is the ...
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1answer
20 views

Transfer Learning on Autoencoders?

I want to use the encoder of my autoencoder for feature extraction in an image anomaly detection framework. For that reason, I thought that pretraining the autoencoder on a large dataset and then fine-...
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7 views

Best practices to name historical data vs the incoming data you make predictions on for production models?

I.e the data you use to train the models vs the data your model uses to make new predictions. I usually name it training vs inference data. But that gets confusing as the training data then gets split ...
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17 views

Machine learning systems where there is a feedback loop between predictions and features

Say you have a machine learning model that predicts whether and online order for an item from a store will be successful. If it is predicted to not be successful at a given time then the item is not ...
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16 views

Implicit feedback ALS algorithm: the alpha parameter

I'm creating a recommender system for a video streaming service. My only knowledge about the user preference on a video is the watched percentage of that video. I'm using the implicit feedback ALS ...
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5 views

How Restricted Boltzman Machine (RBM) generates hand-written digit?

I am reading RBMs from this paper. In Fig1 they show an example of generating hand-written digit using RBMs. This is the figure they are showing: In the learning step first we sample $h$ from $h \sim ...
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14 views

Is it meaningful to regularise a GEV log-likelihood?

Situation/Data: I'd like to start with an example from climate science. Suppose you have a univariate time series $\vec{z} = (z_1, z_2, ..., z_n)^T$, where $z_t$ are block maxima of time step $t\in1,.....
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1answer
38 views

Non-Euclidean analogue to MSE loss

The most basic machine learning model called OLS uses the RSS (squared loss) or its average, mean squared error (MSE), for its loss function, which is aligned with Euclidean geometry. What is the ...
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42 views

Overfitting and underfitting in Neural Networks: Is total number of neurons or number of neurons per layer more relevant?

I have seen posts where the discussion was centered around the effect of big and small total number of neurons in a neural network, especially with respect to the potential of the network to overfit ...
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6 views

Estimation of Linear Dynamical System with diagonality constraints

I am trying to estimate the parameters of the following linear dynamical system \begin{align} X_t &= \phi X_{t-1}+\varepsilon_t, \quad \varepsilon_t\sim N(0, \Sigma_\varepsilon)\\ Y_t & = h^...
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1answer
16 views

Is n_estimators in BaggingRegressor() the number of trees or data subsets?

I'm learning about using the BaggingRegressor() from scikit learn (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html) Its <...
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18 views

Pearson correlation for comparing models [duplicate]

I want to know if I can use Pearson correlation coefficient (i.e. whether my PS satisfies its usage or I need to look at other correlation measures) to study the extent of correlation between 2 ...
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1answer
55 views

What is the probability distribution of a minibatch of data?

Suppose there are numbers $\{1, \ldots, 10\}$ You pick one at random, call it $i$ Then $i$ is a Uniform random variable (https://en.wikipedia.org/wiki/Discrete_uniform_distribution), $i \sim U\{1, 10\}...
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2answers
50 views

Is it Valid to Grid Search Cross Validation for Model Hyperparameter Selection then a separate Cross Validation for Generalisation Error?

The question has to do with Model Selection and Evaluation I'm trying to wrap my head around the scale of how different nested cross validation would be from the following: Let's say I am attempting ...
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15 views

Time-series prediction shifted from the actual

I am trying to predict the AAPL stock price 5-days out from today's closed. I have included technical features like 20, 50 and 200 days moving averages in the price ...
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1answer
27 views

Are Deep Anomaly Detection Approaches able to tell me what kind of anomaly it is?

I recently started to look into anomaly detection. The deep learning approaches are trained on "normal" classes to build a classifier that can detect outliers (anomalies). Are these ...
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1answer
49 views

A queston regarding the meaning of the intercept in regression

Suppose we have a dataset where the indepedent variable $x$ is the work experience in years of an employee and $y$ is his salary in dollars. Such a dataset could consist of the following elements $$(...
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1answer
99 views

How to compute the final misclassification rate in multi-class logistic regression?

Let's say we have the following samples: So there are three labels $y=\left\{1,2,3\right\}$, three binary logistic regression 1-vs-rest classifiers have been learned with model parameters $\beta_1 = \...
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0answers
46 views

Can PCA generate a new random image?

I read The Batch: GANs newsletter and Goodfellow said: My colleague Bing Xu modeled face images from the Toronto Face Database, which were only 90 pixels square and grayscale. Because the faces were ...
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20 views

I Have A Doubt Regarding AdaBoost Weight Update Rule As Various Sources Cite Two Different Things

I was reading about AdaBoost from Hands-On Machine Learning with Scikit-Learn and TensorFlow and came across the formula used for updating the weights. In the book, it was the following: But in this ...
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16 views

Imbalanced Dataset leads to worse results after using balancing methods

I have a very imbalanced Dataset. It's a binary classification. In the train set I have 150,000 times class 0 and 500 times class 1. That's about 0.33% When I train a model like DecisionTree I get a ...
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1answer
10 views

can we perform sub-gradient updates in mini-batches

We are already aware that in case the data is quite bulky, mini-batch gradient descent based approaches may be applied. These approaches load a mini-batch of data, compute the loss on this batch, and ...
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2answers
36 views

Should we use AUC as an indicator of overfitting when dataset is highly imbalanced?

In my problem, there are 2 class labels, but one label only counts for 1% of the total data. I first divided my data set by train_test_split such that only 10% are test set, then I performed 10-Fold ...
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19 views

Which machine learning models get affected by skewness of data?

I want to know which machine learning models get affected by skewness of the data. From what I think, regression model are the one which get affected while others ...
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1answer
57 views

Isolation Forest - Cost function and optimization method

I have two questions about isolation forest. I may not understand how it works correctly but I just wonder: What is the cost function of the isolation forest? What is the optimization method to ...
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10 views

Time Series - AR(2) - Minimum Mean Square Error/Best Linear Predictor?

Is the Minimum Mean Square Error Same as Best Linear Predictor for 1 step ahead predictor? Lets define the Best Linear Predictor as (it minimizes the mean square error) $$x_{n+1}^n = E\left[x_{n+1}\,|...
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1answer
28 views

Deep Learning: Difference at the end of epoch iteration? [closed]

I read this thread. I have a follow up question. As I understand it there is no difference at the end of an epoch or an iteration . To be more clear, at the end of both iteration and epoch changes ...
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19 views

Small sized training set and results varying based on cross-validation split

I need to try to build a classifier based on around 30 instances. The outcome can also be that the dataset is not large enough for this purpose, however I'm not sure on how can I justify this outcome ...
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15 views

What to look out for, when model poorly generalises on previously unseen dataset

I am working on a multiclass, human activity recognition classification problem for datasets collected for the month of April for three different years: 2013, 2014 &...
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2answers
54 views

From sets of elements to distance matrix

I have some sets of elements, $A_1, A_2, \dots, A_k\subset M=\big\{a_1,a_2,...,a_n\big\}$. If two elements, $a_i$ and $a_j$ appear in one set, say, $A_l$, they're supposed to be similar to each other. ...
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1answer
25 views

Padding a time series for neural networks during cross validation

I am trying to train a neural network on some time series data and decided to implement cross validation for my model. The cross validation method I'm trying to implement is the Day Forward-Chaining ...
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1answer
43 views

Does it make sense to regularize the loss function for binary/multi-class classification?

When discussing linear regression it is well known that you can add regularization terms, such as, $$\lambda \|w\|^2 \quad \text{(Tikhonov regularization)}$$ to the empirical error/loss function. ...
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29 views

Understanding Cox regression

I have to work with Cox regression but I'm not getting fully how it works. So I created a very basic fake data sample, and tried to fit a Python Lifelines ...
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2answers
80 views

Is the idea of a bias-variance “tradeoff” a false construct?

The derivation of the bias-variance tradeoff has been discussed pretty well here, see, e.g., https://stats.stackexchange.com/a/354284/46427. I'm, however, skeptical of the existence of such a "...
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14 views

Questions on cross entropy, regularisation and linear softmax

I am working on obtaining skills and bridge gaps in the theory in ML. I pick up course that covers topics I am interested in and for self verification\where I stubmle upon I take a look on how others ...
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27 views

Interpret varimp() with Caret LDA model

I'm using caret's train() function for a binary classification outcome with an lda models, based on several features ...
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29 views

Why do we take mean of errors in linear regression?

I was reading about the probabilistic interpretation of linear regression and the following formula is derived using maximum likelihood estimates : $$ \begin{align*} β=\underset{β}{\text{argmin}}\sum_{...
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0answers
19 views

Manifold Hypothesis vs. Latent Variables Assumption [closed]

As I understand: The manifold hypothesis claims that real world data, although represented in high dimension space, actually lies on a manifold in that space. I.e. that the actual data structure is of ...
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11 views

What approach should be used to model changing conversion rates?

Suppose I want to predict conversion rates of products in an eCommerce web site. Conversion rates can change over time due to changing market condition in addition to seasonality. I can build a ...
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14 views

How to use GLM to achieve binary classification?

I am trying to create a binary classifier using Generalised Linear Models. The package used is the GLM package from H2o.ai. I created the model using various combination of features using the binomial ...
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7 views

Distribution vs Sample F1

Assuming that we have a learned binary classification model $f: X\rightarrow Y$, we can define its accuracy on some data distribution $\mathbb P_{XY}$ as $$a = \mathbb E_{x,y\sim \mathbb P_{XY}}[acc({...