Questions tagged [linear-regression]

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

Filter by
Sorted by
Tagged with
0 votes
0 answers
4 views

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 ...
user avatar
  • 61
0 votes
0 answers
5 views

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, ...
user avatar
0 votes
0 answers
15 views

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 ...
user avatar
0 votes
0 answers
17 views

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 ...
user avatar
0 votes
0 answers
6 views

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) ...
user avatar
0 votes
0 answers
13 views

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 ...
user avatar
1 vote
1 answer
20 views

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 ...
user avatar
0 votes
0 answers
6 views

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 ...
user avatar
1 vote
1 answer
36 views

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,...
user avatar
1 vote
1 answer
16 views

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 ...
user avatar
0 votes
0 answers
7 views

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 ...
user avatar
2 votes
2 answers
18 views

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 ?
user avatar
1 vote
0 answers
21 views

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% ...
user avatar
  • 121
1 vote
1 answer
15 views

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. ...
user avatar
  • 11
1 vote
1 answer
30 views

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 ...
user avatar
  • 207
0 votes
0 answers
16 views

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 - ...
user avatar
  • 1
0 votes
0 answers
7 views

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 ...
user avatar
0 votes
1 answer
29 views

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 ...
user avatar
1 vote
1 answer
22 views

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 ?
user avatar
0 votes
0 answers
6 views

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 ...
user avatar
0 votes
0 answers
3 views

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 ...
user avatar
0 votes
0 answers
10 views

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 ...
user avatar
0 votes
0 answers
13 views

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-...
user avatar
0 votes
0 answers
19 views

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 ...
user avatar
0 votes
0 answers
27 views

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,...
user avatar
  • 209
-1 votes
2 answers
35 views

unable to pass X_train and y_train in my regressor variable. i got a ValueError

...
user avatar
1 vote
0 answers
34 views

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 ...
user avatar
0 votes
0 answers
17 views

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 ...
user avatar
0 votes
0 answers
12 views

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 ...
user avatar
  • 209
0 votes
2 answers
43 views

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 ...
user avatar
1 vote
1 answer
35 views

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 ...
user avatar
  • 302
0 votes
1 answer
14 views

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 ...
user avatar
  • 21
0 votes
0 answers
9 views

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 ...
user avatar
  • 101
0 votes
0 answers
11 views

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 ...
user avatar
0 votes
1 answer
17 views

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 ...
user avatar
0 votes
0 answers
12 views

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 ...
user avatar
2 votes
2 answers
61 views

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 ...
user avatar
  • 121
1 vote
0 answers
23 views

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, ...
user avatar
  • 11
0 votes
0 answers
26 views

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 (...
user avatar
0 votes
0 answers
16 views

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 ...
user avatar
  • 1
1 vote
0 answers
9 views

Linear regresion for multiple time series

I have some data with this shape: ...
user avatar
0 votes
1 answer
18 views

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, ...
user avatar
  • 209
-1 votes
1 answer
61 views

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 ...
user avatar
0 votes
1 answer
24 views

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 ...
user avatar
  • 209
1 vote
1 answer
12 views

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 ...
user avatar
0 votes
0 answers
18 views

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 ...
user avatar
0 votes
1 answer
29 views

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, ...
user avatar
0 votes
0 answers
32 views

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 ...
user avatar
  • 209
2 votes
1 answer
65 views

How to deal with date features in linear regression?

I need some help about a project. I have a dataframe like that; YEAR MONTH INDICATOR_1 INDICATOR_2 INDICATOR_3 2014 3 0.123 0.495 0.222 My goal is to predict all of the indicator for the next year (...
user avatar
0 votes
0 answers
51 views

When I'm trying Logistics Regression, am getting this error "ValueError: Found input variables with inconsistent numbers of samples: [1, 12500]"

Here is my code: ...
user avatar
  • 1

1
2 3 4 5
15