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Questions tagged [regression]

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

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Problems with fixed effects model in R

I have an issue with a fixed effects model for analysing panel data in R, where I get results that I cannot explain to myself. What am I trying to do? I am trying to analyse the effect of the EU ...
DataScience-Student's user avatar
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Overfitting in non linear models [closed]

I have a dataset with 65 data points and 20 features. The problem is related to regression. Initially, I increased the number of data points to 95 using the rbf interpolation method. Then, I tried to ...
Erfan Mollai's user avatar
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R2 is good but not rmse , mse , mae

In my models, R2 in training and test sets are close to each other, but in RMSE, MSE, MAE of some models, these are very different? what is the reason Is there a solution?
Erfan Mollai's user avatar
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2 answers
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Radial basis function for increasing data

I have a dataset with 20 features and 65 samples. The models performed poorly, so I used scipy.rbf for interpolation and added 300 additional samples to the dataset. The models' performance ...
Erfan Mollai's user avatar
2 votes
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22 views

How to properly select features for time series ML models

I've been trying to get good references on how to solve a problem that's been bothering me regarding the modelling techniques I've used. I'm currently interested in making forecasts using ML for ...
loguimaraes's user avatar
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1 answer
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How can I improve my predictive model?

Here is my interpretation of my model so far, I am investigating the relationship between ratings and followers on video games, but there is a problem. The more you get high ratings, the more you get ...
Hugo Guay's user avatar
1 vote
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Are there any general theoretical results about the behavior of data in the neighborhood of a single data point?

I know from calculus that any relatively well-behaved function $y=f(x)$ can be approximated by a linear function $y=ax+b$ within a sufficiently small neighborhood around each point of an independent ...
Vladislav Gladkikh's user avatar
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Why is resnet regression model (on a skewed data with small interval) not converging?

Using resnet50 (torchvision.models pretrained=False) with an input of [15, 224,224] which includes 14 heatmaps and a level set ...
topcat's user avatar
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How to perform extrinsic regression on variable-length time series

I have a signal that describes the the flow in a water pipe. Assuming that individual water consumptions have constant flow rate, each water consumption could also be described by its starting time, ...
broidul's user avatar
1 vote
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34 views

How to deal with a dataset with low feature importance?

I have a dataset whose feature importance lists descendingly as 0.25, 0.16, 0.11, 0.08, 0.05, followed by many features with 0 importance, and the importance drops dramatically. Although the total ...
Yuuya's user avatar
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Finding relationships between features

The dataset I have is quite small (130 samples, 6 features). I am interested in finding relationships between the features. For example, I found a linear relationship between feature1 and feature2. I ...
Shawn's user avatar
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Positional Encoding for FFNN?

Here is my problem: I have input [x1,..,xt,n1,..,nt,1,2,...,t] where there is a missing timestep xi, and I use neighboring time series (found with KNN) n1,...,nt to add more features, as well as time ...
Michel Hijazin's user avatar
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Is it suitable to use residual to assess and tune non-linear regression models?

In linear regression analysis, you would usually use residuals to assess model's fit and examine any potential bias/skewness, or to perform feature engineering based on it. Is it a valid approach to ...
bachts's user avatar
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1 answer
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Data splitting for OLS regression

This is what I have done :: divided my dataset into training and testing sets--> got significant features via. feature selection using sequential feature selector ( MLxtend) on the training set--&...
pomelo's user avatar
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1 answer
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How do I give weight to recent time points when predicting another closeby time point?

I am building a normal feed-forward neural network to predict the value of a masked time point using regression, e.g. I have values for x at times 1, 2, and 4, and I want to predict its value at time ...
Michel Hijazin's user avatar
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1 answer
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Struggling with normalization/Standardisation for machine learning dataset

Sorry for what is probably a very obvious/rookie question. I'm currently doing a data science module for my degree and making very slow progress with the work. The case study i'm doing is around HR ...
Alex Ferry's user avatar
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1 answer
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Build a Neural Network for Multi-output Regression

I have a network model that accepts about 25 inputs and outputs 3 actions. The outputs are: delta X and delta Y of the robot and the angle of the robot. After I enter the data into the model, I get ...
May's user avatar
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Model improvement

Whenever I try to solve some ML problem I get stuck on the first model I choose. I understand the bias-variance tradeoff, but I think it is not the only way to debug a model. Are there any tools to ...
DimitrijeCiric's user avatar
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Extremely Imbalanced and Gapped Dataset in Regression Problem

Currently I am working with a biological dataset with a range of 0-to-1 to do a multi-task regression with Deep Learning. However, this dataset has an empty gap in the range 0 to 0.2 (however there ...
Abdullah Faqih's user avatar
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13 views

Are there any Python (sklearn or other) ML libraries that can handle missing data?

I am running a model using a sparse matrix for the X. It can not be imputed. So are there any libraries who modify the main ML models to deal with this?
J_Bake's user avatar
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1 answer
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How to check if an event affects time series

We have time series data. Depended variable – interest rates, about 15 years, monthly data. Independent variable – event, rating announcement (rating may change or may not), happens 2-3 times per year,...
NoobinStatistics's user avatar
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23 views

Adaptive Lasso Coefficient Weights

I'm trying to understand how the Adaptive part of Adaptive Lasso works. I understand that theoretically, the weights for zero coefficients are inflated to infinity. But can someone explain this ...
user162172's user avatar
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12 views

How can I combine/pool of the results of regression with neural network?

My study has ten imputed dependent variables (plausible values). After separately analyzing each dependent variable using a regression neural network (NN), I must combine/pool the results. I tried ...
minre's user avatar
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32 views

How to compute confidence interval xgboost regressor?

I have time series data to predict values for the next 6 months. I have an xgboost model that predicts the six individual months, for the business what is important is that the cumulative value of ...
tailsrockc's user avatar
1 vote
1 answer
59 views

What's wrong with my implementation of an MLP?

I'm trying to predict housing prices from a Kaggle dataset using an MLP with 3 hidden layers (10 neurons each). Having read about MLPs and backprop in the CS229 notes, I tried to do my own ...
The_Monetarist's user avatar
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Combing Output of Two Regression Models

I have two models. Model 1: I have a dataset of American high school students and their test scores and other characteristics. I built an ARDRegression model that predicts how well a student will ...
Mary's user avatar
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1 vote
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Uncertainty in stacked ensemble model

I am using the stacked generalization scheme to combine the predictions from different machine learning models (input models from now on). I am currently calculating the prediction interval for each ...
umbe1987's user avatar
  • 111
2 votes
1 answer
81 views

Why would having less features work better for LASSO in test?

Given a large set approx 1K features (2K samples), I am trying to find a good regression for my independant variable. As the features are very correlated, I have been drawn to using LASSO. Which does ...
ManInMoon's user avatar
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23 views

Sample Size for Adaptive Lasso

Be gentle, I'm learning here. I have a fairly simple adaptive lasso regression that I'm trying to test for a minimum sample size. I used cross-validated mean squared error as the "score" of ...
JRDubbleu's user avatar
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19 views

Rather than build a classification model, Is building an embedding regression model feasible?

Face recognition models like VGG Face are designed to have a classification head on top and then trained to classify face images, but after they are trained the classification head can be removed and ...
Ahmed Gamal's user avatar
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20 views

How to apply online learnig to Random Forest?

I have developed a random forest model for wind veliocity prediction with hyperparameter tuning, but i am getting continuous data. So i want to apply online leearning for random forest model. could ...
RAJESH KOYI's user avatar
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22 views

Time Series forecasting with SVR

I am trying to forecast my data by Support Vector Regressor, Here is my code: ...
Hadis's user avatar
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1 vote
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118 views

Generating quality synthetic tabular data - is it possible when one's dataset is extremely small?

I've got a dataset consisting of only 17 samples and 6 continuous features (all values in the dataset contain decimals, although 2 features exhibit categorical-ish behaviour). I'm looking at the ...
user23493275's user avatar
3 votes
1 answer
64 views

When do you know training a model is not feasible?

A couple weeks ago I volunteered to take on a project at work to try predicting the ideal price rental cars my employer should be charging based on our historical rental data. The variables available ...
enmasse's user avatar
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How to correctly use gstar package of R for spatio-tempral analysis?

I want to perform a spatio-temporal analysis by highlighting spatial as well as temporal dependencies of the data (I have a 'weight matrix' highlighting spatial dependencies of the counties) on the ...
Shashank Gupta's user avatar
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18 views

How to make my validation plots more stable and improve R2 metric?

I'm working on predicting 4 numeric values basing on signal spectrum (spectrum is represented as an array of 800 numeric values in scale 0 to 1). The input values are scaled by using StandardScaler. ...
mkow93's user avatar
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3 votes
1 answer
162 views

How to fit a product of linear expressions

How can I fit expressions of type $y = (a + bx_1)(c + dx_2)$ using regression?
Prakash's user avatar
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How to visualize correlation values coming from two data frame for comparison

I am working on a project wherein we are comparing two methods used for modeling gene expression. One method is using elastic net and other is using lasso regression. In one method: we see that ...
Rhea Bedi's user avatar
0 votes
1 answer
32 views

What's the best model choice for a non-linear regression task?

I have a dataset with the following format: Rows: 3700, float_columns: 17, int_columns: 2, categorical_columns: 12 Target Type: Continous, float My dataset is an insurance dataset that stores the ...
Connor's user avatar
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0 answers
17 views

Data gaps and linear regression

I've seen somewhere that you can also build a regression for data gaps and just predict them. For example if we have data gaps in $x_{n}$, we can predict it through $x_{1},..,x_{n-1}$. But we will get ...
Ivan Nenakhov's user avatar
2 votes
1 answer
85 views

How to interpret RMSE to evaluate a regression model

I am trying to evaluate a regression model (random forests); my understanding is that R^2 (coefficient of determination) is not a good measure of fitness since my dataset is non-linear. It looks like ...
Shawn's user avatar
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0 votes
2 answers
45 views

Is a Random Forest Capable of Learning and Predicting Numerical Trends in Panel Data?

In a panel data set consisting of exponential functions, each indexed by an integer i ranging from 0 to 100. The exponential function is defined as f(i, t) = A(i) * e^(-r(i) * t), where A(i) is the ...
Emad Ezzeldin's user avatar
1 vote
1 answer
26 views

Building a CNN (with Keras for pixelwise classification)

I have a set of 120x120 input images with 3 channels. I want to build a basic CNN to predict the value of each pixel. I have 2 doubts. One is regarding the last layer - should be a Dense layer, or a ...
Filippo Nunes's user avatar
-1 votes
1 answer
150 views

Can Machine Learning Algorithms Process Contextual Features for Regression?

Take Figure 1 showing point interpolation, where point L0 is being interpolated using points L2 and L1 and the distances L11, L12, L21, and L22. Whilst the graph shows a linear interpolation example, ...
Emad Ezzeldin's user avatar
0 votes
0 answers
24 views

Little to no difference in linear regression plotted line

As I'm learning about basic machine learning concepts, I've learnt about linear regression. Part of my assignment was to implement a linear regression algorithm on a rather simple dataset, consisting ...
Aishgadol's user avatar
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Is there a way to construct the domain hierarchy of a dataset?

Could regression trees help defining the domain value hierarchies for the target variable: for example if we perform a DT regression task over a target variable, could we easily derive the hiearchy ...
SSSOF's user avatar
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0 answers
23 views

Constructing a predictive scoring system for a binary output

I have a system that has several continuous variables as inputs. The output is a binary pass/fail type. The relationship between the continuous variables and the changes of a "pass" is that ...
Cusco88's user avatar
2 votes
2 answers
54 views

Handling Month-over-Month data in Regression Model

I have data similar to what you see in the picture. I want to use a RandomForest Regression model where I can use fields (excluding MONTH_END_DT and LOCATION_ID) to predict REVENUE_PER_UNIT. The idea/...
Larry Burholme's user avatar
0 votes
0 answers
26 views

Using simple RNN to identify a simple dynamic linear system

I have been trying to identify a simple linear second order system (e.g. a pendulum or a mass-spring system), by simulating it in Python using backwards-euler method and then feeding the step changes ...
APasagic's user avatar
0 votes
1 answer
67 views

Effect on regression coefficients by multiplying a constant to a feature

I was solving one quiz question on Coursera and I found an interesting question. If you double the value of a given feature (i.e. a specific column of the feature matrix), what happens to the least-...
teddcp's user avatar
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