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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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Is it possible to overfit a simple single variable linear regression model?

I searched this question and the answer I got was about a general regression model, rather than a single variable, linear regression model. If you increase the number of variables, you could fit a ...
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Can I find the function of X and Y that best predicts the value of Z given a few data triplets?

Given the following data points X Y Z 18.23 3 80 42.2 5.5 600 377.30 52.04 900 6835.86 646.91 17 000 250 20 ? Can I find the function of X and Y that best predicts the value of Z? Is this a ...
lami's user avatar
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What is the best\correct data split approach over time-series data to compare performance of forecasting future data among ML and DL regressors?

Let's say I have dataset contains a timestamp (non-standard timestamp column without datetime format) as a single feature and count as Label/target to predict ...
Mario's user avatar
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How to calculate the coefficients of a weighted nonlinear regresion model

I'm trying to build an Excel-sheet that's able to find the a,b and c coefficient of a simple y=ax^2+bx+c model. This is clear enough by the following math. But the data I'm using is heteroscedastic, ...
DrDirk's user avatar
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1 answer
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Validating ML regression model and predictions

I have a years worth of electricity power data on 15 minute intervals joined with weather data and time-of-week one hot dummy variables. Is using train/test split an okay approach for validating the ...
bbartling's user avatar
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1 answer
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Model/variable gini?

I'm working with a colleague concurrently between R and MS Excel looking at credit risk scorecard modelling. In Excel he has calculated what he says is the gini coefficient for certain variables, ...
StMatthias's user avatar
1 vote
1 answer
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DART algorithm implementation. Converting mathematical notation to pseudocode

I am learning how DART algorithm (https://arxiv.org/abs/1505.01866) works and I want to implement it in C# I have the algorithm's description in mathematical notation and I don't understand most of it....
omike's user avatar
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Regression with time series data that isn't forcasting

I want to do regression on a time series where my output variable is a in the time series. My I have a measurements of a time series $(x_1, x_2, \cdots, x_n)$ and want to predict the variable $y$ ...
TheNumber23's user avatar
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1 answer
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using forecast values from a univariate model as Input to linear regression?

I have weekly time series data for the last 2 years with variables "week", "marketing_spend", "web_traffic", and "revenue" ...
sdave's user avatar
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3 answers
444 views

Regression model doesn't handle negative values

I'm trying to create a model that, given a feature $x_i$, predicts $y_i$ such that $y_i=ax^2_i+bx_i+c$ by using backpropagation. To do this, I'm using the ReLU activation function for each layer. The ...
Iya Lee's user avatar
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Regression with time series data

I want to predict temperature when time (datetime type, hourly data for five months) and humidity is given. Before starting in python, I created a regression model in excel. But instead of predicting ...
Scholar7's user avatar
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Quadratic regression with TensorFlow is not working

I want to make a TensorFlow model that, given features $x$ and labels $y$ such that $y_i = ax_i^2+bx_i+c$, predicts reasonably well the equation. ...
Iya Lee's user avatar
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1 answer
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how to chose between these models?

i have a regression problem so i tried some regression models in order to pick the best one (based on RMSLE) here are the results: here are all the models = [ ('LR', LinearRegression()), ('Ridge', ...
99_zz's user avatar
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1 answer
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Maximizing minimum correlation

What meaning has the weighted sum of a group of variables so that each weight is assigned to maximize the minimum resulting correlation of all these variables to the sum obtained?
gabriel's user avatar
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1 answer
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What are the constants in this formula for polytrophic head?

Polytrophic head can be expressed as H = b1N^2 + b2NQ + b3Q^2 where b1, b2 and b3 are constants, N is the speed of the compressor (rmp), and Q is the volumetric flow rate of natural gas at the ...
Starbucks's user avatar
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Why is my predictor value (continuous) perfectly correlated with my logit value (when testing logistic regression model assumptions)?

Question: Why is my predictor value (continuous) perfectly correlated with my logit value (when testing logistic regression model assumptions)? Code: ...
In_cognito's user avatar
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2 answers
349 views

How do we distinguish between correlated and un-correlated features/variables ? Is it relevant for a regression analysis?

Correlated and un-correlated terms are frequently used in data-science and understood as if they represent correlation coefficient. Is it the right way?
Subhash C. Davar's user avatar
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Evaluation parameter in knn

I am using KNN for a regression task It's like that: 1- I normalized the data 2- I calculated the distance of the new data with the previous data (Euclidean distance) 3 - I choose k nearest neighbors ...
Ho3ein H K's user avatar
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0 answers
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why CNN the model can't predict 0

I have two datasets: force plate data and plantar pressure data. The force plate data consists of 6 data points, while the plantar pressure data consists of 90 data points. Both datasets have a ...
stack offer's user avatar
0 votes
1 answer
335 views

Can I change the number of inputs to a keras model while preserving the trained existing weights

I have a simple Sequential keras model with 150 Inputs. Some of these are simply OneHotEncoded values. Now I would like to add more options to the OneHotEncoder. As an example: I previously had Blue, ...
FLOROID's user avatar
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1 answer
371 views

What loss function to use for predicting discrete output sequence given a discrete input sequence?

I am working on sequence-to-sequence tasks where the input is an n-length sequence of discrete values from a finite set S (say ...
NotNotLogic's user avatar
1 vote
1 answer
149 views

Can I use a fitted polynomial regression to make reverse predictions?

I want to start off by acknowledging that this may be a dumb-sounding question to someone with more machine learning experience to me, so please go easy. Here is the background. I am currently an ...
Gabriel Kelly's user avatar
0 votes
1 answer
57 views

Forecasting on multiple timeseries data with limited data points

I'm predicting operational expense of a stores of a company. I have only six years of data per store at a daily granularity. I want to train a model to predict the next years operational expense. In ...
NAS_2339's user avatar
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How to handle multiple multivariate timeseries?

I am trying to develop a model using machine learning that reproduces a biological behavior. My goal is to do a regression of timeseries e.g from multiple input each time_step predict multiple output :...
Ketchup's user avatar
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Classification-Problem based on limited Dataset (Need keywords to search/ reading recommendations)

I have a dataset of about 200 test subjects, each with age, testscore and a one of two possible traits. I want to define some kind of function, where I input age and testscore, that predicts which of ...
inane's user avatar
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1 answer
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high degree polynomial model with sklearn does not fit

The idea was to gradually raise the degree of the polynomial. Here is the code that implements creating a random dataset, fitting the polynomial of the CHANGE_ME ...
Dranikf's user avatar
0 votes
1 answer
37 views

How to aggregate the metrics from two different regression problems?

I'm about to conduct some tests to compare two solutions to regression problems. And to make the results more robust, I want to apply both on a few different datasets (all problems will be a ...
Mehran's user avatar
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-1 votes
1 answer
31 views

How to predict Total Hours needed with List as Input?

I am struggling with the problem I am facing: I have a dataset of different products (Cars) that have certain Work Orders open at a given time. I know from historical data how much time this work in ...
can.inan's user avatar
0 votes
1 answer
268 views

Best package and function in R to use to replicate my (Backward & Forward) Stepwise Regression results I got using step from the stat package

I am doing a research project as a 2nd author on a paper exploring the properties of a novel algorithm for Optimal Variable Selection where I am running the benchmark Variable Selection Methods. Each ...
Marlen's user avatar
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1 answer
134 views

Why does softmax perform well on MNIST but poorly on EMNIST letters?

I am learning about softmax regression using Dive into Deep Learning. I have a very basic question on why softmax performs well on one dataset and poorly on another. I tried modifying the results from ...
user676464327's user avatar
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0 answers
56 views

Adding Data sequences as unique data on dataset for regression model

I want to predict a force plate using plantar pressure. The shape of the force plate data is a 15000x6 array, and the shape of the plantar pressure data is a 15000x89 array. I will use a regression ...
stack offer's user avatar
1 vote
1 answer
69 views

Dictionary-based text analysis- dealing with length

I am working on an analysis using a dictionary-based text-as-data approach. I have a dataset of texts (n=1200), and I am applying a dictionary of 50 words (I tokenize the text with each word being one ...
Iamembarassed123's user avatar
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0 answers
66 views

Neural network to predict Y = x1*x2*x3

My training data consists of 3 variables X1, X2, X3 such that ...
40pro's user avatar
  • 111
1 vote
1 answer
79 views

revenue forecast using regression - what is the input for future?

I have a dataset with quarter wise revenue for past 3 years from Jan 2020 to Dec 2022. I have 4642 customers. Each customer has 1 row of data which includes features based on his purchase frequency, ...
The Great's user avatar
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1 vote
1 answer
357 views

Regression to fill NA values

As a part of an exercise, I have the following dataset. Note that I have no idea where the values come from (are they based on something real or are they random numbers? Don't know...) ...
Davide_sd's user avatar
  • 111
0 votes
1 answer
49 views

How to build model if the data dont have corelation each other's

I have 2 datasets, call them dataset A and dataset B. Then I want to predict dataset A using dataset B as input using regression model. dataset A format: dataset A shape(15000,1) dataset B format: ...
stack offer's user avatar
1 vote
1 answer
814 views

How to compare $R^{2}$ of train and test data in a Deep Learning Neural Network Regression model?

I want to judge the goodness of my neural network regression model built using Keras Python Library. The problem is the following: from an input like (1000, 5000) so 1000 samples and each sample has ...
HelpNeederStudent's user avatar
1 vote
1 answer
56 views

A good 4th Benchmark method to compare the performance of a novel Variable Selection Algorithm being evaluated

I am collaborating on a research project with a respected econometrician as a graduate student (although only in an MS program, not PhD program mind you) exploring the properties and comparing the ...
Marlen's user avatar
  • 167
1 vote
1 answer
170 views

why CNN model can't learn well the peak from data

here I have two different datasets. dataset1 is force plate data and dataset2 is plantar pressure data. dataset1 has shape (2050,2) and dataset2 has shape(2050,89). before doing the training I have ...
stack offer's user avatar
0 votes
1 answer
26 views

How to isolate a clear relationship from a subset of data with lots of noise and outliers

I am doing an analysis of aircraft data and I want to see how much fuel is burnt on landing. There are 2 main factors aircraft type and landing time (ie. time elapsed) However there is a cheeky third ...
Jay Ramsay's user avatar
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0 answers
11 views

How to find confidence sets for a bunch of regression functions?

Unlike confidence intervals where we are interested in indicating a range of beta values in which the true parameter lies 95% of all times, I would like to understand how confidence sets are ...
TFT's user avatar
  • 135
1 vote
1 answer
36 views

Can the product of tree regressions be represented by a single tree?

Assume that we have two separate tree regressions. I'm interested in understanding whether the product of tree regressions can be represented by a single tree. Would this be possible?
TFT's user avatar
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0 answers
30 views

Is there a difference in result if we apply Polynomial / Kernel Regression on mean of target data, or all data?

Let's say we have some data : input data X with shape (1, N=100), this will be duplicated 1000 times. target data Y with shape (S=1000, N=100). We have 1000 experimental data points, samples. My ...
user143751's user avatar
0 votes
1 answer
69 views

SARIMAX model for predictiong the next 100 days

I have daily weather data for almost 50 years for a weather station, and I want to predict the next 100 days' weather. I use python and all of the tools I've used so far (pmdarima.auto_arima, ...
3 months's user avatar
0 votes
2 answers
2k views

Which standardization technique to use for Lasso regression?

I am fitting a Lasso Regression to do feature selection in my dataset. I have seen it is common practice to use StandardScaler to standardize the dataset. However, given that the distribution of my ...
StephM's user avatar
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0 votes
1 answer
108 views

HalvingGridSearchCV

Is there a way to get Feature importance from sklearn`s HalvingGridSearchCV? For example: Is there any way to access the feature importance? Please help me up. Thanks!
Sanket's user avatar
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1 vote
1 answer
92 views

Input shape - How to feed metadata to a ML model?

I have data such as metadata: hospital layout, number of rooms, number of patients in a day etc. and then I have data regarding the doctor’s check-ins. Which is more granular. How do I feed this data ...
StephM's user avatar
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1 vote
0 answers
16 views

what causes the model can not predict using other data

I am here using ResNet50 to create a regression model. I ran into a problem when I wanted to test a model using other data. The length of the dataset is 2050. Then I separate it into training and ...
stack offer's user avatar
4 votes
1 answer
156 views

Force positive coefficients for Logistic Regression and LinearSVC

Do you know what is the best way to force positive coefficients with Logistic Regression and Linear SVC using scikit learn? for instance ...
Alex's user avatar
  • 165
0 votes
0 answers
300 views

Extremely poor prediction: Resnet50 Regression

I tried to implement ResNet50 model for Regression prediction. Below is my trial code. This code runs without error. You can also try it without dependency. how to improve so that the model is able to ...
stack offer's user avatar

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