Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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Deriving Prediction Intervals for Orthogonal Distance Regression using `scipy.odr`

Questions How can I derive prediction intervals for predictions based on new observations from the output of scipy.odr? Is it also possible (or necessary) to take ...
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How to build regression model with restricted range?

I am working on a (NN) regression model for a scientific sensor. With regard to the model, I am primarily interested in the positive outputs of the sensor. For the negative outputs, I only care that ...
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Is it important to split the data in train-validation-test when using XGBRegressor? [closed]

Is it important to split in train-validation-test when using XGBRegressor in order to avoid the possible problems caused by overfitting?
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Regression performance with Feature Selection

I would like to ask you a theoretical question. In my project I am trying to get a better performance from my regression model by feature selection methods, especially with CatBoost feature ...
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1answer
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Predicting products to be sold in a store - problem formulation

I have a data from a store for the products that sold since more than 5 years. Each sell process has a customer id, date, and the quantity of the product. I want to build a machine learning model to ...
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Searching for Multitarget regression dataset [closed]

input variable 3-10(data type : Numbers) target variable 2-10(data type : Numbers) Number of data points more than 2000
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Using Random Forests as a GAM

This is a regression problem. I have a dataset of sales of various products overtime. I have three kind of feature sets : Price features, ...
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Finding the correlation between business distribution and the unemployment rate in COVID times [closed]

I have business distribution data of US counties (i.e. how many establishments exist in each sector in each county) and county wise unemployment rate since January 2020. I want to analyse the impact ...
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1answer
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Creating dummy variables [closed]

I have a Eurobarometer dataset and want to eventually create a logistic regression model and a linear probability model using a set of dates as the dependent variable. However, the dates in the ...
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Should I use RMSE or Average Euclidian Distance here?

I am tackling a problem in which robots have an actual position in 2D space, as well as a believed position (where they believe themselves to be, based on unreliable measurements they make). The ...
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Regarding Keras mean_squared_error losses

I am trying to do a RandomizedSearchCV for a simple regression task. Essentially two inputs to one output. Upon inspecting the resulting models, it appears that the 'best' model has a ...
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3answers
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How to convert regression into classification?

So I have a regression problem with bunch of features X, and labels in the amount (price $). How can I convert it to classification problem? I have read about convert label from continuous to ...
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Imbalanced classification or Regression? What is the best approach to my A/B testing related problem?

The context of the problem is A/B testing of two new versions of a game. I have a structured dataset (50000 rows x 22 columns) from the game designers that represents data with respect to two versions ...
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What is Happening in the training process when we are fitting a model to the data [closed]

In any prediction task, the process of “fitting” a model to the data observed in the training process can be best described as... Assessing all observations available and then backsolving for the ...
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How to normalise(?) an [x,y] time series data set

Problem: I'm currently parsing a time series dataset, of [x,y] coordinates. The data isn't complete - it contains gaps and jitter, and I would like to fill these ...
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Using flow_from_dataframe with multiple inputs

I was wondering if it is possible to use the flow_from_dataframe on Keras with multiple inputs. I have a Dataframe with 3 ...
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What is the best way to train a gradient boosted model on a binomial dataset where the number of “observations” for each instance varies?

I have been trying to figure out the best way to train a gradient boosted model on a binomial dataset. To be more clear my dataset is in a format similar to this: [link to toy dataset]. https://i....
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1answer
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Alternative regression model algorithms for machine learning

I am looking for not so known regression models and if possible a python library that implements it. In my quiver I have: Generalized Linear Models, Linear, Lasso, Ridge... Decision Tree based model: ...
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Measure correlation for categorical vs continous variable

Given a variable which is categorical that depends on continuous variables, I would like to know how to check wether these continous variable explain the categorical one. So: ...
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Why does regression model predict the same output

I have built a CNN for regression, but it is giving identical predictions (up to 8 sig. fig.) for almost 1/3 of the test data set. (The other outputs are different.) Is there a reason why this might ...
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1answer
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Fitting multiple line

Short version: How can I find a function that maps X to Y when data looks like this. Note: For a pair of emissivity and distance relation between temperature and raw_thermal_data is linear. Long ...
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1answer
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Classification or regression problem?

I have a table with this features: ...
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How to assess nested cross validation results in comparison to non-nested results?

I have a nonlinear regression model scoring genes from scores between 0 to 1 as to whether they are likely to cause disease. Training data is ~700 gene samples by 53 features. Currently I get results ...
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2answers
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Confidence rating for regression tasks

In classification tasks, we can interpret the output vector as how "confident" the model is that the input has a certain label. For example, ...
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Time series Classification vs prediction: terminology

Consider a dynamical system of the form $dx/dt = y(t) = f(x)$. Iterating the system $f(\cdot)$ will generate a time series: $y(1),y(2),...$. When the general underlying eq model $f(\cdot)$ is unknown, ...
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1answer
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Cleaning a certain feature to predict salary using Machine Learning

Info: I am working on a dataset, and i would like to create a model that would predict salary. Columns are as follows: ...
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1answer
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decision -tree regression to avoid multicollinearity for regression model?

I read in comments a recommendation for decision tree´s instead of linear models like neural network, when the dataset has many correlated features. Because to avoid multicollinearity. A similar ...
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Improve confidence interval accuracy

I am doing a linear regression on log-transformed data and I use the bayesian approach to model the predictive distribution and construct my 90% prediction Interval. The problem with this approach is ...
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interpolation - graphical quality evaluation

I try to compare different interpolation models quality and I'm looking for a graphical tool to do that. Application case: I'm not familiar with intepolation using neural networks. I decide to test it ...
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1answer
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Modeling price vs demand

I have a dataset consisting of products, clients, price policy, discounts, quantities, and net sales. The task as put in words by the business is quantity vs price. I have noted a few observations ...
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What metrics to use in regression if variance in output label is very low?

What metrics/method should one adopt in judging the error when variance is low in output variable. To give you an example : the output variable can be stock prices over a month, the variance generally ...
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Inverse predict the features from known target with fitted sklearn regressor

I understand that the default way a scikit-learn regressor works is that we fit it to a dataset of features and targets (X_train, ...
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5answers
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How many ways are there to check model overfitting?

I am running xgboost on a regression classification problem where the model is predicting a score of how likely a gene is to cause a disease from 0-1. I try to avoid overfitting in all the ways I can ...
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1answer
24 views

Using Low Frequency Labels with High Frequency Features

I am trying to build a model (most likely a regression or random forest regression) for quarterly financial data. My training data has a daily cadence, but I am not sure how to work with these to ...
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1answer
18 views

regression network that will be optimised on a subset of the data

I am trying to optimise my network that is trying to perform regression. Currently, the dataset is dominated by values in a certain range. It is very good at predicting these values. I want to try to ...
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1answer
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Appropriate objective function and evaluation metric when I DO care about outliers?

I am reading these two pages: xgboost documentation Post on evaluation metrics I have a dataset where I am trying to predict future spend at the user level. A lot of our spend comes from large ...
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Can Boosted Trees predict below the minimum value of the training label?

I am using gradient Gradient Boosted Trees (with Catboost) for a Regression task. Can GBtrees predict a label that is below the minimum (or above the max) that was seen in the training ? For instance ...
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ground truth fit is worse than cross validated fit on noisy data?

I am having these weird results when playing around with cross validation that I would greatly appreciate to have any comments. Briefly, I have a lower mean squared error (MSE) when doing regression (...
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How can a CNN account for spectro-temporal constraints in neural data?

What are there the best ways to leverage the unique "geometrical" constraints of spectro-temporal signal representations (architecture, filter shapes, data augmentation, etc.)? For example, ...
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Using Vector Auto Regression for multiple time series at once

Say I have a dataframe like so: ...
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1answer
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No gradients provided for any variable: ['Variable:0', 'Variable:0']

I am using Python 3.6 and Tensorflow 2.0 for the following code for linear regression: ...
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1answer
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Relationship Between Independent and Dependent variables

I calculated the distance correlation among the independent variables and the dependent variables to verify the nonlinear relationship among the variables and the values I am getting for each ...
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2answers
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How to calculate steady/incremental growth?

I have timeseries data for stocks at minute intervals. What is the best way to calculate incremental growth, for example if I have a stock's price from 9am to 2pm at minute intervals, how can calcule ...
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KNN regression in R

I'm studying a non-linear regression problem and I have read that K-nearest neighbors regression performs well in this case, is there an R implementation of this regression algorithm?
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Adjusting Variables in Multiple Linear Regression

Suppose I have $10$ exposure variables $a_{1}...a_{10}$ and one dependent outcome $y$.I suspected $a_{9}$ and $a_{10}$ as a possible confounding variables. So at first I performed the multiple linear ...
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Are there any techniques to explore the inter - target correlations and the input - output correlations in the Multi Traget Regression Problem

I am working on the Multi target regression problem in which there are 4 target variables and 17 input features. I tried to use hard parameter sharing method to predict the target variables. I am ...
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Multidimensional time series regression

I’m new to time series forecasting and I’m trying to implement regression models using both ARIMA and LSTM for a multidimensional and multivariate time series. The samples are indexed by time, ...
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how to improve score of automl regressor

I was trying to solve a regression problem on HackerEarth. I got to a score of 81.20 using XGBRegressor after some data preprocessing, the top ranker had a score of 81.55. Some one suggested me to use ...
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Time Series Predictive Model

I have a dataset similar to the following one: ...

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