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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Generate Fake Data to get around GDPR Constraints

I have a project based on AI and Machine Learning. Unfortunately, I am constrained with time and due to GDPR(General Data Protection Regulation), the process of getting data will take a long time. Is ...
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Time series data in regression analysis

I'm making a regression analysis in Python to find out the dependence between the stock price and several variables : my dependent variable - share price of company, independent variables - price of ...
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With Stationarity How can ARMA Modelling have any Validity?

I have recently been thrown into the deep end with time-series econometrics. The first thing I have learned is that in order to avoid the spurious correlation trap, I need to ensure that all the ...
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Boosting usa bootstraping?

I had a question about boosting. When in the first iteration of the algorithm we pass our data to the first decision tree, this data we pass is a sample generated by bootstraping or is it the original ...
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Chicken and egg problem in machine learning [closed]

Recently, I went through an ICLR paper SELF-LABELLING VIA SIMULTANEOUS CLUSTERING AND REPRESENTATION LEARNING. In the paper, authors discussed simultaneously labeling the images and training a network ...
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Regression analysis with time series data

I'm completely stuck. I'm making a regression analysis in Python : my dependent variable - share price of company, independent variables - price of steel, price of coal and changing in local currency. ...
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Mini batches and loss in recurrent neural networks (RNNs)

Suppose that we have a sequence $\left\{x^{(k)}\right\}_{k = 1}^{N}$ and that we wish to use a RNN to predict the next element of the sequence given the previous elements of the sequence (e.g., a ...
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Why is a random forest regressor better than a random forest classifier when predicting a category?

I am building a model that recommends the optimal golf club based on data I have gathered. Since the model prediction should be a category, ie. a golf club, I would assume I would have to use a ...
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Is a model with xs squared a good fit for a parabola? [closed]

It should be, but the fact that the xs and not the parameters is squared confuses me.
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Choice Between Alternatives in Machine Learning

I need some advice on the simplest/best way to structure an ML model for a (slightly) non-standard situation. Setup: I have many teams in a company that have leaders. Each team has two options for a ...
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Model predicts same number for any input on initialization of random weights

My PNAConv (pytorch) network has been having issues with predicting the same exact value for all inputs. Without getting into too many details, I have a broader neural netowrk question. When I ask my ...
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Likelihood-ratio gradient estimator in linear dynamical system in python (Jax)

TL;DR I am trying to implement the likelihood-ratio gradient estimator in a linear dynamical system (LDS) with Gaussian transition noise and Gaussian observation noise I am currently using python and ...
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Comparison of normality + similarity distribution of time series datas

I have a training time series data , whose last 20% is kept as validation data. I want to check whether the distribution of training and validation features are similiar and normal, so that we can say ...
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How to predict a mathematical progression with keras

I try the following model for a many-to-many recurrent network: ...
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How to improve F1 score with LocalOutlierFactor

The classification we are working on is to predict the investment strategy the company is using by NLP, and there are four types of strategy. “Balanced Fund (Low Risk)”, “Fixed Income Long Only (Low ...
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Statistically increase mean difference between two data sets?

A dataset will be used to train a binary classification model. For better understanding/visualization, the data set was divided into 2: one set with all the rows that result in prediction value of 1 ...
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A question on the projection step in Generic Adaptive Method Setup: $x_{t+1} = \Pi_{\mathcal{F},\sqrt{V_t}} (\hat{x}_{t+1}).$

I am reading the paper "ON THE CONVERGENCE OF ADAM AND BEYOND". In this paper, they proposed the following framework of adaptive methods. I was confused on the last step: $x_{t+1} = \Pi_{\...
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How to deal with very little data

I thought I understood the train/validation split basics but this question got me confused. I could acquire very little data. I don't have it already, which is why I cannot just experiment with it. I ...
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What to do with 99% F1 score in binary classification?

I've been handed a binary classification model to look after. The model uses the F1 score for comparison purposes. The challenge is that the F1 score against the test dataset is very high, like 99%, ...
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Universal approximation of Gaussians

Can gaussian kernels reproduce non continuous L2 integrable functions? ( Do non continuous L2 integrable functions lie in the RKHS constructed by a Gaussian Kernel?) Edit: I think my question is being ...
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"SMOTE makes the assumption that the instance between a positive class instance and its nearest neighbors is also positive"

I am trying to get my head around this assertion by Liu, Y. et al (2011 pp. 7) about SMOTE oversampling technique that: because SMOTE makes the assumption that the instance between a positive class ...
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Are machine learning algorithms flexible enough to learn changing feature importances?

I have a prediction problem where each row/entity contains data over a range of time and features can change in importance over time even for a single entity. I am wondering if machine learning models ...
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Change in Correlation after Imputation of Missing Outcome Data for Machine Learning Prediction Task

I am attempting to generate a predictive model for a continuous outcome using machine learning models. However, some observations in the original dataset have missing outcome data (and missing ...
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How to handle weighted examples in stochastic gradient descent (with mini-batches)?

Suppose I have $M$ data points $x_i$ and associated weights $w_i > 0$. I want to optimize a function, $$F(\theta) = \frac{1}{M}\sum_i w_i f(x_i;\theta)$$ in the parameters $\theta$. I will assume ...
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True and Estimated Posterior of a Mixture of Gaussians in Bayesian Learning via SGLD

I am trying to recreate one of the experiments in this paper, (Bayesian Learning via Stochastic Gradient Langevin Dynamics). To be exact experiment 5.1. I am pretty sure, I am missing something here ...
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how to deal with data leakage in historical data

I have a dataset containing matches from 2000 TO 2018 and I am asked to predict match outcomes for the year 2017 to avoid data leakage I am going to just train my model from 2000 to 2016. in the ...
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Why is cross entropy loss better than MSE for multi-class classification? [duplicate]

I know there's a lot of material on this, but I'm still struggling to find a scenario where cross-entropy loss is better than MSE loss for a multi-class classification problem. For example, if we have ...
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How can a feature that when removed, does not affect the model's performance not be declared unimportant?

In the paper on the Boruta algorithm, there is a statement that is unclear to me (highlighted in black). The all-relevant problem of feature selection is more difficult than usual minimal-optimal one....
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What does Feed Forward Policy in terms of Reinforcement Learning?

While reading Reinforcement Learning, I saw the term, Feed Forward Policy and the article also says that it does not have memory, what does "it does not have memory" also mean? If possible, ...
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Collinearity problem

Consider a linear regression of this type: height: beta_0 + beta_1*weight. Adding BMI as parameter would add complexity to the problem or just cause a collinearity problem?
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9 votes
1 answer
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What can be done about assumption violations in logistic regression?

I am working on a logistic regression solution, and I'm experiencing some issues with assumptions according to the diagnostic graphs.For linear regression, I am familiar with addressing similar ...
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Model performance when ground truth is not available

I am building an LSTM Autoencoder (unsupervised model) to detect anomalies in a time series dataset. The input is telemetry data from routers and I want to detect anomalies in the throughout of router....
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What if you had a feature column that was the same for every row for a specific date when using XGBoost with time-series data?

Imagine an XGBoost model trying to predict business revenue or performance. Imagine a dataset that looks like this: Date | Business | Revenue | ...more Business properties... | NASDAQ Price | You'd ...
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Why use transpose of nabla in gradient descent

For gradient descent we have the formula: $f(x_{k}+d_{k})\approx f(x_{k}) + \nabla f(x_{k})^T d_{k} $ What I don't understand is, why we use the transpose of nabla and not just nabla.
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validation and calibration of crop yield data using conditional inference trees

I am trying to validate and calibrate the conditional inference tree model using the crop yield data, and I started by splitting my dataset into training and test sets. After splitting, I had to ...
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Don't understand why SelectKBest with Chi Square does not involve p-value

According to SelectKBest's documentation page, it 'select features according to the k highest scores', which in this case would be the Chi Square score. https://scikit-learn.org/stable/modules/...
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Metrics for imbalanced multi-class classification [duplicate]

I am looking for informations about metrics for classification with 3 unbalanced classes. I have following numbers of samples in every class: 1 As you can see two classes are quite balanced and one is ...
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how to find standard deviation of sampling distribution without knowing population parameter σ

Standard deviation of sampling distribution is given as σ/sqrt(n). But usually the population parameter σ will be unknown. In that case, how do we calcuate the standard deviation of sampling ...
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Is Platt scaling applicable to a small sample size?

I am learning predictive modeling and recently came across the calibration technique called Platt scaling. I want to ask: Is this technique applicable to the small sample size such as my project (n=...
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Tensorflow - calling a model inside a GradientTape scope VS calling it inside a loss function

Is there a difference in the gradient computation between the two code snippets ... Code 1: ...
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Predicting repeated binary response from contacts over time

From their initial entry date, people must be contacted each month for three months. It often takes several unsuccessful attempts before you establish contact. Once you reach someone, contact attempts ...
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What are the advantages of using a Machine Learning (NN) method instead of regression model in survival analysis?

Suppose that I have a sample of survival times $t_1,...,t_n$, censoring indicators $d_i = I(t_i < C)$, and covariates $x_i\in{\mathbb R}^P$. Suppose that I have a flexible parametric regression ...
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2 votes
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Adj. $R^2$ with tree ensembles

Consider tree ensemble methods such as XGBoost, Lightgbm and/or Catboost. Is the adj. $R^2$ a valid metric for tree ensembles? I'm curious because these methods handle factor variables differently. E....
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Baseline model for predicting the load forecast

I have a model which uses the historical data to predict the electricity load consumption. I want to compare my model with a baseline model to show the performance, however I can not find a good base ...
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evaluating scoring metrics during hyperparameter tuning

I'm struggling with a couple of concepts related to hyperparameter tuning. I'm developing a model (gradient boosted tree) in python using sklearn. Currently, I'm in the process of using sklearn's ...
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2 votes
1 answer
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Logistic Regression on multiple classes (Shouldn't it be only on binary?)

I'm a bit confused with the usage of logistic regression for multi-class classification. My understanding is that a logistic regression is dichotomous (two possible classes), so in the example of the ...
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1 vote
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Predict most probable document in a given set of documents by a given question

I would like to know / discuss which implementation would be the best solution to predict to a given question the document from a given set of documents which is has the highest probability of ...
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build a linear regressor with labels in different scales

I just ran into this linear regression problem where the labels are in entirely different range for example for 25% of the samples, the labels are in [0.001,0.01], then for another 25 % of the samples,...
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Estimate the value of a sigmoid function over expectation

I would like to estimate the function value of the sigmoid over an expectation, that is: \begin{equation} \sigma(\mathbb{E}_{p(x)}[f(x)]), \end{equation} where $\sigma(x) = \frac{1}{1 + e^{-x}}$, and $...
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Conditional independence proof

I want to prove that $\mathbb{P}(X|U,P) = \mathbb{P}(X|U) \implies \mathbb{P}(X|U,P,T) = \mathbb{P}(X|U,T)$ Where all the letters denote random variables. I'm not sure that this is right, but it seems ...
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