Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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What to do when test values are not correlated with predictions?
I have a regression problem where I obtained a mean absolute error close to the desired value but the predictions do not correlate well with the expected values. I have tried several algorithms, ...
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Convert Regression output to classification based on prospective performance of model
Let’s say I have a regression model that predicts experimental value in the range of 4 to 8 of an object based on a set of features. I am aiming to design an oBject with an experimental value of great ...
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Feature selection: ANOVA between features vs within a feature
I am currently performing feature selection on a dataset containing continuous and categorical features. The target is a continuous variable.
If I understand properly, ANOVA can be used between ...
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How do I use ML models to estimate current stress level based on past data?
I am new to machine learning and I cannot understand the difference between estimating current stress level and predicting future stress levels based on historical data. I have been told these are two ...
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Understand and compute confidence interval and coefficient of variation for regression model
I would like to better understand the concepts of:
coefficient of variation and confidence interval.
Trivially taking the definitions from wikipedia:
confidence interval (CI)
In frequentist ...
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Can the k-index in either of the two individual locations be used to predict the estimated kp-index?
The k-index measures the condition of the magnetosphere. It is usually averaged over three hour, so each day has 8 measurements.
The planetary k-index (kp-index) is an average of the measures taken ...
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Logistics or linear regression for a regression task involving outputs between 0 and 1
Problem
Consider a regression task of mapping inputs $X$ to outputs $y$ where $y \in [0,1]$.
Two linear models that we can use to model this input-output relationships are logistic regression $f_\...
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Model for small sample time series
Im a total novice and i need to estimate some kind of relation-proof model(Granger test results, correl matrix are already provide some evidence) with following dataset:
20 observations (2001-2021), 4 ...
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How to build a regression model with same targets for different observations?
I'm working on a project involving around 90 3D-printed cubic samples with different structures. After conducting a compression test, I obtained stress-strain curves with 700 data points for each ...
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Training multi-variate LSTM model with sample observations with differenet mean values
I am developing an LSTM model to predict the force-deformation response for wind turbine blades. I have generated the training data from a high-fidelity model for wind speeds ranging from 3m/s to 25m/...
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how to build model using two input dataset in which there is no common column to merge or combine
I want to create model for truck company in which trucks delivers the car for customers.i have two data sets. one is customer details like how many cars they want from particular area or terminal and ...
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Converting a Standard LSTM RNN over to a Transformer Model
I am looking for some advice on converting my existing CNN/LSTM RNN over to a Transformer type model. This regression model takes a sliding window size of 240 rows with 33 features. It aims to ...
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Can lag features be applied into test data without label?
can lag features be applied into test data without label? I've been wondering. I tried to build random forest model using dataset: training data (with label Y) and testing data (without label Y). The ...
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What is the reason behind high frequency output from LSTM model?
Following is the time history response of my input features, which has relatively low frequency component
My LSTM network architecture is as follows:
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Array column in dataframe as input for classification models
Please consider the following dataframe:
My challenge is with the column 'geometry_coordnates'.
I've got a column 'geometry_coordnates' that is an array of numbers. It can be a representation of a ...
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Can reducing information improve regression prediction?
Variable A is either 0 or 1. It is 0 if the sum of variables a + b + c + d … is less than some constant threshold, and is 1 if the sum of variables a + b + c + d … is greater than some constant ...
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Product Incrementally Estimates From Regression
I have a bunch of Point of Sale (POS) data at store/SKU level for a few years and was wondering if it was conceptually possible to run a regression to obtain sales incrementally estimates for each SKU ...
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Standardizing my target vs not standardizing
I've heard from multiple sources that it depends on whether I should standardize or not. Most of the time, people would say it doesn't make sense to do so, some would say it's better if I standardize ...
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Is there anyway to evaluate the estimation results of least square
Consider the scenario where a practical problem is tackled utilizing the method of least squares. Upon each iteration, an estimation of the parameter $\theta$ is derived via $\hat{\theta} = (X^\top X)^...
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Can a multivariate MIMO LSTM forecaster learn the relationships between the multiple feature outputs?
Question: Can a multivariate MIMO LSTM learn the relationships between the multiple feature outputs?
This question arose when I decided to modify a multivariate (Multiple Input - Single Output, MISO) ...
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With infinite observations, would the weights resulting from ridge regression be the same as simple linear regression?
As the number of observations approaches infinity, do the weights of a linear regression approach the weights of a linear regression with L2 penalty?
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Align Vectors are Easy to Learn?
I have three vectors $x,y_1,y_2\in\mathbb{R}^{n\times 1}$, where $x=y_1$, $x\perp y_2$. If I use $x$ as input of a 2-layer perceptron, will regressing $y_1$ be easier than $y_2$ (i.e., when fully ...
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What is the right type of equation to fit the following data
I want to estimate the amount of unique social media users $N$, in a list of the $K$ most recent social media posts (i.e. I extract the user that posted each post).
I've made a few graphs based on the ...
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Does MAPE really penalizes overpredictions?
I read it on many sites, that one of the main "disadvantage" of MAPE is that it penalizes overpredictions, hence it prefers models that are under-predicting.
The main argument is that if we ...
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Linear Regression and Logistic Regression
I'm a beginner, and I'm wondering whether a logistic regression in a nut-shell is just normalizing a linear regression? Correct me if I'm wrong, but I came to this conclusion because the predicted ...
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How to build a categorization system without a target variable?
The data
I have a large dataset containing execution logs from various tests conducted over several years. The logs can be noisy and often contain a plethora of messages detailing the ongoing ...
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Minimize MAE loss for a target that is sum of two other targets
Working on a regression modelling task where my dataset have some feature columns, two more columns A, B and a target column T. The goal is to predict T, and minimize MAE, that is ...
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How to assign sample weight for regression problem
I'm trying to model a forecasting problem where I'm trying to forecast for the following month. I am using LightGBM Regressor class for it and it giving me a decent ...
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GridSearchCV with TimeSeriesSplit
I am tuning the HPs for a time dependant neural network model (heating energy consumption prediction) in a bachelor thesis.
Total amount of samples is 145.860, with minutely granularity from January ...
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Metrics for difference between two "vectors" of independents values
New here!
I'm working in some crypto for a Ph.D. and I'm trying to figure out the best metrics to measure the different/error between a input vector of integers with the output that ideally is the ...
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The optimal way to stratify a numerical target variable into a categorical one for a machine learning algorithm
I have tabular data, the predictive variables are numerical and categorical and the target variable is a numerical one. Using the proper techniques I can make predictive models with R^2=0.95.
Now let'...
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How is it called when instead of creating predective models finding patterns in observed data (ML) you tried to guess the model theorically...?
I'm a college student appasionated of machine learning and I've decided to my bachelor thesis about it. I thought that as an interesting introduction to machine learning, I could introduce it by ...
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RMSE of whole and part of test dataset
Can anybody help me to understand the behavior of metrics (RMSE namely) when testing model? I have NN with 1 hidden layer for regression task. RMSE equal 0.07 for external test dataset. But if I break ...
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warning 'newdata' had X row but variables found have Y rows
Linear Discriminant Analysis (LDA)+logistic regression model
lda_model <- lda(train_labels ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = train_data)
LDA scores for the training ...
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Automating the task of figuring out if a task is classification or regression
When manually identifying if a given dataset and dependent variable are suitable for classification or regression I look at the type of variable (continuous or discrete) in which the name and values ...
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Neural regression predictions all around the mean of target
I have a transformer regression model and some data about last users transactions (categorical and numerical). My target has exponential distribution with mean aroud 10e4 and also zero-inflated, so I ...
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Regresson on 3d tabular data
There is a dataset, where each Y depends on separate table :
Each table consists of the same set of columns, but the number of rows may vary.
Features in tables are both numerical and categorical (...
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2 regression models giving same performance
I have 2 regression models (1 is deeplearning based) and another is SVR both trained on the embeddings obtained from the last FC layer of ResNet50. Output variables are min-max normalized to get in ...
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XGBoost tweedie regression objetive from scratch
I'm trying to gain a deeper understanding of the tweedie loss function and how it is used in XGBoost. So, I tried to implement it from scratch. I started by examining the original implementation.
I ...
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calculate the predicted value based on coefficient and constant in python
i have the coefficients and the constant (alpha). i want to multiply and add the values together like this example. (it has to be done for 300000 rows)
Prediction = constant + (valOfRow1 * col1) + (-...
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Can you please provide one fully solved gradient boosting regression numerical example (not python code) [duplicate]
Can you please provide one fully solved gradient boost regression numerical example (not Python code).
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One-Hot encoded variables dominates importance among other variables
I am currently training some machine learning models to predict the 28-day compressive strength of cement, a continuous real-valued variable. The available dataset comprises samples from three ...
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Best metrics to evaluate the performance of a regression model?
I've just started with machine learning and I have a lot to learn but one of the recent problems I'm facing is evaluating the performance of a regression model. I know about MSE, RMSE, MAE ...
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Grouped Time Restricted Demand Regression with value cap
so I am working on quite an interesting regression task that I haven't encountered before.
Our company sells products (steel) in tons. We offer contracts where the customer orders a certain amount of ...
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XGBRegressor underestimates sum of regression target (insurance application)
I'm trying to learn how to apply Boostig Algorithms (e.g. XGBoost) to insurance applications (premium calculation). As a starting point I used this tutorial from the scikit-learn website.
tldr: The ...
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Parameter estimation in linear regression
Another test Q I couldn't answer -
We have marks of students belonging to 3 sections - A,B,C and two genders - M & F. Which regression model will not be able to estimate all the parameters?
1 ) ...
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Is it possible okay to use regression MLP for ordinal classification problem when target variable is numerical?
I have a target variable of 1-10 that represent difficulty level. These are individual classes represented by integers with 1 being the easiest and 10 most difficult. I have decided to use regression ...
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Is this problem a time series regression or seq2seq regression or some other type of problem?
I measure sequences of 3 parameters in my system. 2 are independent and the 3rd dependent. Let's call the independent ones $x$ and $y$, and the dependent one $z$. They are each measured once per hour ...
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Predict demand, given price, if data is a list of single-item sales
Is there a way to predict demand given some price if the data, that I have, looks somewhat like this:
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(timecourse) regression analysis help
My research aims to investigate the effect of earthquake events across the United States on visiting healthcare locations (i.e., people living in the same city where an earthquake has just occurred ...
