# Questions tagged [linear-regression]

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

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### Why is a neural network not doing better than multivariate linear regressions?

I am making neural networks of multiple targets, all using same training data. For some of these targets, multivariate linear regressions do a very good job, i.e. a strong linear relation exists ...
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### SciKitLearn - Powerlifting Placing Predictor Recommended Models?

I am new to data science and working on a project utilizing the openpowerlifting database to create a machine learning model to predict what someone would place in a local powerlifting competition, ...
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### How to Incorporate Upward Trend into XGBoost Time Series Forecasting

I'm working with an XGBoost XGBRegressor model right now, attempting to utilize it to predict time-series forecasted data. My dataset is not publicly available, so I will use general terms to describe ...
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### Is a simple linear regression appropriate for an originally ordinal outcome variable?

Context: To form an index, I summed (and weighted) 2 variables containing ratings (1-9). Potentially problem: Wondering if it is appropriate to conduct a linear regression, all other assumptions being ...
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### I need more than one model to help me understand the relationship and the effect between the variables [closed]

I intend to study the impact in the short and long term of three dependent variables (overall financial development index , financial market development variable, and institutional development index) ...
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### Data Sets for _Pricing Analytics_ by Paczkowski?

I am working through Pricing Analytics by Paczkowski https://www.amazon.com/Pricing-Analytics-Walter-R-Paczkowski/dp/1138623938/, and it would help me greatly if the data sets for the various examples ...
1 vote
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### Calculate RMSE based on R squared and vice versa

If for example I have the value of RMSE can I calculate the $R^2$? And vice versa if I have the value of $R^2$ can I calculate the value of RMSE? I have all predictions, dataset, training set, and ...
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### Stacking ensembles in meta learning with only one base algorithm

I'm learning stacking ensembles in meta learning and , there is an example where thy used only lightgbm as base model and linear regression as meta model, they first Split thé dataset into 50 samples ...
1 vote
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### Linear regression returning negative values for house price prediction

I am trying to do a prediction of real estate (prices are in millions). The mean price for the dataset is 4 million. I do not have any negative values in my dataset,...
1 vote
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### Why can Random Forest "handle missing values and cardinality well compared to linear regression"?

I've read a question comparing Linear Regression and Random Forest Regression. I was supposed to choose between then and solve a problem (=predict prices). The question mentioned that "Random ...
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### Estimating effect of coefficient in log-linear model

I'm working on a log-linear model which has multiple variables with their coefficients. The model estimates a variable log(Y) ~ X. I'm trying to estimate the effect ...
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### Why do we don't write units with MAE or RSME for regression problem ? If I wish to write the units when how do I identify the units for them?

I have referred many research paper but no one is talking about the units of the metrics. Do MAE , RMSE etc have some units ?
1 vote
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### Why would a Linear SVR model greatly outperform a Linear Regression model on model stacking

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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### Get result from log transformed variable

I can't find some documentation. I had right-skewed target (sale price) variable and also some skewed features at the same way. I did log transformation and fit the regression model and it doing well. ...
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### What is the right way of training Regression model having various categories involved?

I am working on one regression problem statement and it involves multiple categories into it. I am not sure how to proceed with it, hence looking for your guidance/suggestions over it. Suppose there ...
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### Why do i need to write regressor.predict(x_train)?

Im currently learning data science and i was unable to understand a particular part in linear regression model. The following is my code - ...
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### how to tune hyperparameters inn regression neural network

hope you are enjoying good health,i am trying to built a simple neural network which has to predict a shear wave well log values from other well logs,but my model's is stuck in mean absolute error of ...
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### What Equation is model.coef_ Derived From? (SKLearn)

Fairly simple question, but something I've been unable to understand firmly by scouring the interwebs. After running a LR model using SKlearn, one of the key outputs is ...
1 vote
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### My Linear Regression Model Mean Absolute Error(MAE) is 0.29 and R2 0.20 , Is this a acceptable Model?

My Linear Regression Model Mean Absolute Error(MAE) is 0.29 and R2 0.20 , Is this a acceptable Model ? How can increase the r2 score ?
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### confidence interval around standardised regression coefficient?

I have computed a simple linear regression model as below, but am confused as to whether the confint() function is sufficient to provide 95% confidence intervals around the standardised regression ...
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### SKLearn - Different Results B/w Default Linear Model and1st Order Polynomial Linear Model

SUMMARY I'm building a linear regression model using Scikit and noticing that the model "performance" (RMSE and max error, namely) varies depending on whether I use the default LR or whether ...
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### Recommendations for modelling panel data

sending positive wishes to y'all. I have about 10 years of growth rates in real estate prices and some other macroeconomic variables such as inflation, unemployment rates, fuel prices, growth in ...
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### How to Approach Linear Machine-Learning Model When Input Variables are Inconsistent

Disclaimer: I'm relatively new to the data science and ML world -- still trying to get a firm grasp on the fundamentals. I'm trying to overcome a regression challenge involving a large, multi-...
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### Make fitted xgboost or linear regression model predicts values in thé future

I have a machine learning model that I fitted with xgboost and linear regression. My dataset has thirteen features and has price as the target. Is there any way to ...
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### Feature importance of a linear regression

What is the easiest and easy to explain feature importance calculation for linear regression? I know I can use Shap to compute feature importance, but I find it difficult to explain it to stakeholders,...
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### Multi Linear Regression on String Values

I'm using datasets which involves mostly of string values. The main outcome of the project is that it should predict success. Now I can use OneHotEncoding to convert string values in numerical format ...
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### Interpreting Homoscedasticity and Residual spread for linear regression

I am new to Data analytics. I have difficulties of understanding what both the Homoscedasticity and residual histogram is trying to convey. Please any help is appreciated. I have a linear regression ...
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### Effect of size of data on the confidence on the coefficients in Linear regression?

What is the impact of size data on the confidence (p-value) of model coefficients?. Does increasing the size of data always improve the confidence in the model coefficients? Suppose I have 100 data ...
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### Training data in sentiment analysis

I'm doing sentiment analysis of tweets related to recent acquisition of Twitter by Elon Musk. I have a corpus of 10 000 tweets and I'd like to use machine learning methods using models like SVM and ...
1 vote
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### How to interpret a linear regression effects graph?

could someone tell me how to interpret the following graph? It corresponds to a graph in which the effects of the variables in a linear regression are observed, but its interpretation is not clear to ...
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### Is there a way to forecast a time series multiple linear regression using externally made dummy variables?

This question concerns question 4h of this textbook exercise. It asks to make future predictions based on a chosen TSLM model which involves an endogenously (if i'm using this right) made dummy ...
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### Automatic scaling and resizing

I have a CAD-like system: users create Canvases and put different Objects on it. Sometimes users need to scale the Canvas and move all included objects to different positions and probably change their ...
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### Real World Regression R & p-value

I am trying to figure out whether our customer support has an impact on tickets opened by customers. Our employees should contact customers to avoid that a user will open a ticket. The data is quite ...
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### Which statistical analysis to use when you assume non-linear model but not-specified?

I'm a psychology student/researcher and looking to model a phenomenun in which there are 3 variables. The relation of these variables are exactly unclear but I assume these variables are non-linear in ...
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### Multicolinear Predictors Effect on Model

I know that multicolinear predictors in a model aren't ideal because it causes the model to be sensitive to very minor changes, which then reduces our ability to interpret the effects of each ...
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### Difference between OLS and Gradient Descent in Linear Regression

I understand what Ordinary Least Squares and Gradient Descent do but I am just confused about the difference between them. The only difference I can think of are- Gradient Descent is iterative while ...
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### Need help to understand the formula of gradient descent with multiple features

I am trying to implement gradient descent with multiple features after listening to Andrew Ng's Coursera lecture. gradient descent for multiple features So for example when calculating for theta 1, ...
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### When linear regression is really a good model to be used?

By definition of linear regression model: If we note $Y$ the real random variable to be explained (endogenous variable, dependent or response) and $X$ the explanatory variable or fixed effect (...
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### What is the good way to print classifier lines with sklear learn LinearSVC

I've tried to make a multivariate regression with LinearSVC and I have seen two ways to print the lines of the classifier, and they haven't the same output. I have seen one on this forum and the ...
1 vote
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### Linear regresion for multiple time series

I have some data with this shape: ...
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### How do I decide the frequency of data capture for modeling? How does it affect my final model?

I plan to capture data to predict energy consumption in a food processing plant. I want to capture production details such as how much each category of food is produced, what is the machine's output, ...
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### step-by-step creation of models with accumulation of predictors vs GridSearch

Can you please tell me if step linear models of the independent variable "ols_step_both_p()" (R) are possible with the accumulation of predictors in the amount of 58, 220 and 299, naturally ...
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### What are the business consideration while creating features?

I'm creating a model to predict energy consumption in one food production facility. From business, I know that Downtime due to power failure, machine failure and maintenance, etc. is one of the major ...
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### How to perform linear regression on a parameter that represents state/configuration of a machinery in a production process?

I am trying to perform linear regression on a manufacturing process in order to determine the influencing parameters on a particular product. The thing is there are several production parameters, and ...
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### Extracting Linear Trend from Time Series Data

I'd want to show that the behavior of our customers with the most customer support follows a different trend than our overall customers (with less support). As you can imagine, a linear fit to Time ...
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### scikit-learn: feature analysis differs heavily from model coefficients

I am trying to perform linear regression and I want to analyse the available features beforehand. The task is to predict the value of a house. Some of them might have a high impact on the label, ...
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### How does missing an important feature affects the feature importance of remaining features in the model?

I am creating a linear regression model for energy usage in a food processing plant. Unfortunately, I don't have the historical data for one of the critical features (I know it is important from ...
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