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

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1answer
7 views

Down-sampling the data

I'm trying to do a regression analysis with two different datasets. One of them has 965 samples and the other 2275. I wish to downsample the latter to 965. Please suggest ways to do so. Thank you!
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1answer
9 views

High RMSE and MAE and low MAPE

I have used a few regression models on the same dataset and obtained error metrics for them as shown below, The RMSE(Root Mean Squared Error) and MAE(Mean Absolute Error) for model A is lower than ...
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2answers
12 views

When should ordinal data be represented catigorically and when as integer?

I am doing the Kaggle competition House Prices: Advanced Regression Techniques to learn more about data analysis. I would like to apply multiple models to the data(Regularized LR, Random Forests, ...
1
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1answer
18 views

Logistic Regression or regression SVM for probability of outcome

I am working on a prediction question: what's the percentage of Y = 1 using a number of features? The output Y values I have for training are in binary. In this case, should the prediction be ...
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1answer
20 views

Multiple Regression Outputs from neural networks [on hold]

I have a regression problem which I have to predict 10 numerical values from a provided data. For example let's say I have a data set containing `X1, X2, X3, X4, X5, X6 ...2001Q1 ,2001Q2 ,2001Q3 ,...
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1answer
5 views

Train neural network for regression with negative samples

I have training samples which have have vector $\vec x$ as input and a vector $\vec y$ as output - both vectors have real (float) numbers $\in \mathbb R$ as entries. I want to train a neural network ...
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0answers
26 views

Timeseries sensor data for one process regression modeling

The dataset: I have a dataset containing the data of a manufactoring process. The dataset contains the process ID ("Sarzs_no"), the ID of the manufactoring machine ("Unit"), the data logged by two ...
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0answers
12 views

How to assign prior probabilities while using Gaussian Process bandits?

I implemented the work based on Srinivas, "Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design" and it looks like my code is working. My problem is that when the ...
2
votes
2answers
24 views

Is it a good idea to normalize the outputs of a Neural Network for Regression, when the different outputs vary in magnitude?

I understood that it is not necessary to scale the output of a neural Network when I predict a single value via regression. Is it necessary do normalize the Outputs of my neural Network if I have ...
3
votes
1answer
20 views

Interpreting the Root Mean Squared Error (RMSE)!

I real all about pros and cons of RMSE vs. other absolute errors namely mean absolute error (MAE). See the the following references: MAE and RMSE — Which Metric is Better? What's the bottom line? How ...
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votes
2answers
17 views

Preprocessing of Sudoku Dataset from Kaggle

Dataset: https://www.kaggle.com/bryanpark/sudoku I would like to create a neural network for this Dataset. Feature: ...
2
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0answers
14 views

Other types of regression models in sklearn

I want to make full and interaction model in python using sklearn. Is there any way to make such models. Because sklearn only provides library for Linear, polynomial regressions and not for full and ...
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0answers
5 views

Comparing two regressions that differ by a few data points

I have built a model that explains how much risk of the stock market (S&P 500 index) is attributable to each sector, where each sector is independent from each other (correlation coefficients ...
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1answer
31 views

Modelling promotions for demand forecasting

I am trying to develop a model to predict future demand for a product. Now, there are always some promotional events that affect the sales. I am trying to solve this problem using dummy variables. ...
1
vote
0answers
11 views

How to get maintenance interval from maintenance outcomes?

I have a machine, which needs maintenance. Every time the technician visits the machine, there are four possible outcomes: a) The machine is broken, b) The machine is still running, but it's high ...
1
vote
0answers
14 views

Applying LIME to a regression Keras model with categorical variables

I have trained a simple Neural Network with 64 categorical features. This is a Q-learning model with 4 outputs each corresponding to an action reward. I'm trying to explain the best action (maximum ...
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0answers
9 views

Robust method to extract “pure” part of a variable

I am trying to perform a multi linear regression model: $$y_i = β_0 + β_1x_{i1} + β_2x_{i2} +... + β_px_{ip} + ε_i$$ where $$x_{i1}, x_{i2}, ..., x_{ip}$$ are highly correlated with each other (VIFs ...
1
vote
1answer
8 views

Preventing fitting Regression CNN to the mean when dataset has only few outliers

I am trying to train a CNN for regression on a dataset where most of the points lie around a similar output value. There are however a few outliers that are very important but they are less ...
0
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0answers
14 views

Reducing the dependency between variables

I am trying to perform a multi linear regression model: $$y_i = β_0 + β_1x_{i1} + β_2x_{i2} +... + β_px_{ip} + ε_i$$ where $$x_{i1}, x_{i2}, ..., x_{ip}$$ are highly correlated with each other (VIFs ...
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votes
0answers
8 views

Does it make sense to preprocess (normalise or standardise) this data for GAN?

I'm working on a project where I have a dataset for a dynamical system (pendulum) containing a trajectory, energy cost and corresponding control actions (See below). I'm using a generative adversarial ...
0
votes
1answer
18 views

Finding the equation for a multiple and nonlinear regression model?

Regarding nonlinear and multivariable regression, I use R or Matlab. In the case where a regression with just two variables, I simply draw the graph Y with respect to X, and look for the equation of ...
1
vote
0answers
12 views

Using neural network regression for skewed data

I'm using a feed forward network for a regression problem where the response variable is a ratio that can be negative and is very heavily skewed. As the response can be negative, I can't just log ...
1
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1answer
10 views

How the term “R-squared” in VIF(variance inflation factor) is different from normal R-squared calculation?

In normal calculation of R2 , more the value of R2 , it indicates variable represents more variance across the dataset. But in the calculation of VIF (variance inflation factor), higher the value of ...
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0answers
41 views

Evaluation of regression models with different evaluations (MSE, variance, VAF etc.)

When comparing several regression models in terms of quality, it seems like most have agreed on the MSE. There are also papers comparing "variance" and "variance accounted for (VAF)". However, there ...
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votes
1answer
15 views

Do we need the testing data to evaluate the Model Performance - Regression

I have been working with Classification Modelling in R and Python for the last 6 months now. With the Classification, the evaluation of the Model was based on Precision, Recall, Hamming Loss, accuracy ...
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0answers
25 views

Feature selection after performing PCA

I have a data set with 57 variables on which I am performing PCA. The PCA returns me a list of principal components, each of which is in turn a list of the loadings to be placed on my variables in a ...
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0answers
13 views

Difference between Robust Regression and Ridge Regression

As I read through definitions and documents, Robust Regression and Ridge Regression seem to be the same thing to me. Just want to check with some experts to see are they exactly the same or there are ...
1
vote
1answer
31 views

Neural Network unable to track training data

I am new to ML and this is my first Tensorflow project. I am doing regression with Neural Networks on a dataset with 17 features and 1 outcome. But for some reason my network is unable to follow the ...
0
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0answers
12 views

How to measure halo effect of a campaign?

Let's say that I have two campaigns, that they both advertise the same product. There are two options: Either these campaigns run at the same (option A), or these ...
0
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0answers
6 views

How to build a sentence qulaity estimatior?

I am working on a problem where I need to predict sentence quality(say if a sentence is well written then 10,moderately written then 5 and if too many mistakes or poor formation 1/2 on a scale of 1-10)...
-1
votes
1answer
25 views

Interpreting ANOVA results

I have 2702 records with one target variable (Y) and 11 independent/predictor variables (X1-X11). I am doing multivariable regression to understand if I can predict Y using X or if there is any ...
1
vote
1answer
20 views

How to do multivariable regression in Orange3?

Orange3 contains a number of regression widgets, but they all seem to be univariable i.e. one independent variable that correlates to one dependent variable. When I have more independent variables ...
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1answer
30 views

CNN only performs well when split into 2 models [closed]

I have 2 groups of data of equal shape (the main difference between the 2 are that one has half as many features - and consequently different labels of course)that perform better when they are trained ...
1
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0answers
29 views

What is the Time Complexity of Linear Regression?

I am working with linear regression and I would like to know the Time complexity in big-O notation. The cost function of linear regression without an optimisation algorithm (such as Gradient descent) ...
0
votes
1answer
19 views

What is the best statistical measure tool to measure how close data is to fitted regression line if outliers are not fitted

I am using a custom algorithm based on Gradient descent which computes the best fit on a training dataset. In this data set I have outliers i.e. data points that I do not want to fit. The algorithm is ...
1
vote
0answers
18 views

Nested cross-validation for regression over small dataset

I'm trying to do nested cross-validation for regression model parameter selection and prediction evaluation. I'm using temporal data (series of count). The problem is that I don't have a lot of data ...
0
votes
2answers
17 views

Interpreting Results of Multivariable Regression / how to transform variables to improve results

I am working on a project that predicts the Market Cap (value) of different crypto-currencies. My data is very small (51 observations) and I initially have 18 X-variables. I was hoping to get feedback ...
0
votes
2answers
22 views

Influence of a data point on the regression result?

Let's say I perform multiple regression where y = income, x1 = educaiton, x2 = sex, and x3 = religion from 2003 to 2018, where the data is measured daily. Is there any way to quantify an impact of a ...
0
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1answer
23 views

Can I use regression to solve a multiple equation problem

I'm working on a problem which is a multiple equation. I have a group of people and each person in the group is working on different tasks (e.g. n tasks in total). Each person in this group is working ...
0
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1answer
31 views

How do I plot linear regression results if input and target have different sizes? [closed]

For a linear regression model that I conducted, I'd like to review the regression plot of results. But since I have an input of size 6 parameters and target (output therefore) of 4, I get error when I ...
0
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0answers
16 views

Is there a method to always overshoot in regression? [duplicate]

I'm creating a regression model to predict some quantity. If the model predicts a value lower than the actual value, it incurs a monetary cost. If the model predicts far too high of a value, it only ...
0
votes
1answer
19 views

How to evaluate RMSE with standard deviation

I have regression model, where target is between 0 to 1. standard deviation of target is 0.817 and RMSE of model on hold out is 0.52. I am wondering if this good model or not. Any feedback will be ...
1
vote
1answer
31 views

Regression model for continuous dependent variable and count independent variables

I am currently learning R and I am relatively inexperienced in the field. Hope I can get some advice from you guys! I am working on a project where I have to estimate the average processing time of ...
2
votes
1answer
18 views

alternatives to regression to decide weights in an expression

I have a use case in which I am required to predict variable y which depends on 5 variables, xi. Consider something like [ w1*x1 + w2*x2 + w3*x3 + w4*x4 + w5*x5 = y] This expression doesn't ...
0
votes
2answers
50 views

Predicting number of cars

I am predicting the number of cars from a traffic dataset. Here is my data dictionary : The ‘Traffic-Major-Roads(kilometres)’ file contains the following variables (variable names are in bold): ...
1
vote
1answer
33 views

Can GAN generate very happy facial expression

I have some dataset ${(x1,y1), (x2,y2)...(xn,yn)}$, where, $x$ is the picture of a facial expression,while $y$ is the fraction corresponding to their degree of the happiness (happy laugh: $y$ close to ...
2
votes
3answers
51 views

Why replacing null values with outliers?

I have been watching a tutorial on stock price prediction with multivariate linear regression and the tutor replaces missing value data, NaN, with the outlier -99999. Why and how do replacements like ...
0
votes
1answer
48 views

How (and why) are random forests able to represent both linear and non linear data

From reading articles I've read that random forests are suited for representing both linear and non linear data quite well, but I can't find an explanation as to how and why it has his flexibility? ...
1
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1answer
51 views

Algorithms, techniques, papers for regression with vector output

I have a regression problem that has relatively low dimensional input (say 8 initial relevant features, without the engineered ones), but very high dimensional output vector (not a single value for ...