Skip to main content

Questions tagged [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
5 views

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
0 votes
1 answer
13 views

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
0 votes
1 answer
32 views

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
  • 1
0 votes
0 answers
19 views

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
0 votes
0 answers
26 views

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
0 votes
0 answers
10 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
  • 1
0 votes
1 answer
13 views

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
0 votes
0 answers
19 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
0 votes
0 answers
11 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
  • 1
0 votes
0 answers
13 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
35 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
0 votes
0 answers
8 views

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
  • 1
1 vote
0 answers
23 views

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
0 votes
0 answers
15 views

Lost when trying to get good time series prediction results (regression problem) even after trying many things

I'm not able to get good results after a long time testing when using TensorFlow to predict time series data (regression problem). I don't know if the problem is with the data (little quantity and/or ...
Marco's user avatar
  • 111
2 votes
1 answer
77 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
0 votes
0 answers
19 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
0 votes
0 answers
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
0 votes
0 answers
19 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
0 votes
0 answers
19 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
  • 1
1 vote
0 answers
114 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
  • 31
0 votes
0 answers
13 views

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
0 votes
0 answers
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
  • 1
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
  • 131
0 votes
0 answers
13 views

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
29 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
  • 631
0 votes
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
74 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
  • 33
0 votes
2 answers
39 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
22 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
144 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
  • 111
0 votes
0 answers
25 views

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
  • 17
0 votes
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
53 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
60 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
  • 165
0 votes
0 answers
27 views

How to reduce number of continuous variables before I make a set of best predictors (for handgrip strength in women)

My assignment question is quoted: "2. Which set of variables best predicts handgrip strength in women? a. Reduce the number of continuous variables before doing the analysis." I do not ...
Nathan Vermaerke's user avatar
0 votes
0 answers
11 views

Convolutional Neural Network for Forecasting Video, 3 Dimensional Data, 2 Spatial Dimensions, 1 Time Dimension, Tensorflow

If we have data with 2D RGB images ordered in time, how can they be fed into a CNN? Are there 4d arrays or something like multidimensional pandas Dataframes compatible with TensorFlow. The output, I ...
user22233907's user avatar
1 vote
0 answers
25 views

Time-series Forecasting model for License monitoring

I am trying to build a forecasting model to predict the number of used licenses for an application-feature combination in the future. The data (time-series data) in which at a point in time, the '...
Sherwin R's user avatar
0 votes
1 answer
76 views

Stock Price Prediction Using Random Forests (R-squared problem)

...
Kemit4's user avatar
  • 103
0 votes
0 answers
22 views

LightGBM Regressor miscalibratred/underestimating on high fitted values and overestimating on low fitted values

I'm training a pretty standard LightGBM regressor and noticing a strange pattern with the residuals (see images below--I'm bunching the predicted values and taking the observed average for the group). ...
dfried's user avatar
  • 101
2 votes
0 answers
18 views

How do I achieve MAE < 8 using ResNet50 on this dataset?

I've been up all night trying to achieve MAE < 8 for this age recognition dataset: https://people.ee.ethz.ch/~timofter/publications/Agustsson-FG-2017.pdf It is for an online class I am taking, the ...
YKY's user avatar
  • 121
0 votes
2 answers
68 views

Best model for regression in this case?

I am doing some modeling to predict a variable of interest given a big set of features (500) for which I expect a considerable amount of interactions happening at least among some of them. I first ...
Mirko's user avatar
  • 111
0 votes
0 answers
13 views

Demand Forecasting methods for hundreds of time series

I have TFL cycling dataset for the time period from JAN 2023 to JUNE 2023. I would like to forecast the demand or expected no. of trips for each station at every hour of the day. Post some data ...
a_jelly_fish's user avatar
0 votes
1 answer
24 views

Are my regression metrics value correct?

So im using a dataset for Wine Prediction where im using Linear Regression model to predict the prices. These are the steps i'm using: ...
Rushabh Kayadra's user avatar
0 votes
1 answer
46 views

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, ...
wander95's user avatar
  • 101
0 votes
0 answers
13 views

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 ...
ChemDev's user avatar
  • 101
0 votes
0 answers
63 views

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 ...
Fred vh's user avatar
1 vote
1 answer
49 views

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 ...
user123456789's user avatar

1
2 3 4 5
32