Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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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?
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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 ...
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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:
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Problem of constant shift in prediction for neural network regression model with gradient-domain loss function
I'm training a regression model using neural network which is trained on MSE of both output and spatial gradient of output. With some simplification, the model is:
$$
y = f(\mathbf{x};\theta)
$$
where ...
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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?
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The following error came up ValueError: No gradients provided for any variable: when running the code
The code is predicting future number of confirmed cases of meningitis. Back propagation neural network was adapted for the model, i tried to run the program but the above error was displayed and a ...
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why by adding additional information as number of sequence on dataset can avoid overfitting
I am developing a regression model to analyze walking styles. The dataset I am using to build the model is from 2 different sources, let's call them dataset A and dataset B. Dataset A has a shape of (...
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How to make a DL price prediction model aware of market fluctuations [closed]
here's a pretty general question, so i do not expect to get precise answers. Instead i hope to read ideas and suggestions.
I have a deployied proprietary Deep Learning model that make retail price ...
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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
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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 ...
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Framing a probabilistic time dependent problem
I need help framing the following problem:
I have a dataset where I know for each day, at customer level, that someone with device X bought device Y.
Example:
At day 1
50 people with device X bought ...
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Finding a relation between three variables
I am new to data mining and have learnt about association rules mining, classification analysis, cluster analysis and outlier analysis. So, to find relationship between three variables, regression ...
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CNN model well trained but can't predict real data
I'm developing a CNN regression model for gait analysis. It seems the model is well trained, with low val_loss and low loss. However, the model does not work well to predict real data. In this ...
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why MSE will be high when I inverse data
I make a regression model to predict force plate using plantar pressure. I am trying to use CNN model in this case. I have 2 different datasets, dataset A (force plate data) and dataset B (plantar ...
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Python Impute using BayesianRidge() sklearn impute.IterativeImputer regression impute analysis value error
PROBLEM
Use interativeImputer from sklearn.impute.IterativeImputer, to get regression model fit for for BayesianRidge() for impute missing data in variable 'Frontage'.
After the interative_imputer_fit ...
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Can Pybats' Analysis function make a prediction on a future DateTime object that is only one step beyond the final point of the existing data?
I was able to utilize the Bayesian approach of statistics in Pybats in order to make a forecast model on a timeseries dataset. While the model is learning from the ...
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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, ...
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Neural networks and input filters
In my use case scenario, I have a neural network that should filter the input and pass a specific value of the input array to the output.
In particular, let's define the input as:
...
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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 ...
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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 ...
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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 ...
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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 :...
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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 ...
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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 ...
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How to structure dataframe combinations for regression, without corruption/loss?
I have a data set, redacted sample below. My goal is linear regression. My question is: Have I created unintended results, due to how I structured the df, using concat and/or div?
For example, ...
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Oddly classifier is more accurate than regressor for solving a regression problem - what could be happening?
I am working through a simple tabular supervised machine learning problem.
I have a continuous target variable y that is normalized to the interval 0-1 to represent ...
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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 ...
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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 ...
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ANN regression on percentage (continuous count) values
in 'classical statistics' I would possibly use a glm (family = negative binomial) for a regression with tabular data (x = count values, categorial values etc.) on percentage values as output (y).
This ...
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Weighting Regularisation Term in Aleatoric Uncertainty Loss Function
I am currently digging into Uncertainty Quantification and try to implement Aleatoric Uncertainty estimation into a regression model.
Given this publication we can model the Aleatoric Uncertainty by ...
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Difference between recommender system and appetency score
I'm wondering about the difference between the recommendation system and the appetency score. I already know that the appetency score is a binary classification problem for one product where we try to ...
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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 ...
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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 ...
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Independent variable(s) showing no relationship with dependent variable(s)
I have a simulated set of data for a gas turbine where dataset looks a like this,
As seen from the scatter plots there isn't much of relationship between the dependent variables and independent ...
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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 ...
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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 ...
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Neural network to predict Y = x1*x2*x3
My training data consists of 3 variables X1, X2, X3 such that
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How to actually begin using my first EC2 instance on AWS in order to run regressions too big for me to run locally
I am using the RStudio Server Amazon Machine Image (AMI) for a collaborative statistical learning research project intended to produce a paper for publication because its computational requirements ...
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Plateaus training loss but validation loss decreases
I am training a fully convolutional regressor, with mobilenet as its backbone. I have already overcome a massive overfitting problem by augmenting the data. However, the training loss seems to be ...
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If possible in a regression model, is splitting up the target variable to more target variable (using multiple models) have any drawbacks?
As in the title, my main goal is to have one or more boosting regression model for a target variable(s). Let's call the main target d . I can split it up like <...
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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, ...
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Same precision, specificity and sensitivity values
My Model gives giving same precision, specificity, and sensitivity values when I'm running a loop five times to fill missing values using Random forest regression and for classification using Gradient ...
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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...)
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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:
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How to select best kernel_size and max_pool_size in CNN1D
I have data with shape size 1,89. setup kernel_size = 3 and pool_size = 2 on the conv1d layer. However, the model is not able to predict the peak well. i think the problem is because the kernel_size ...
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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 ...
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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 ...
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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 ...
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What does KDE plot tell to me?
What the KDE plot tells to me? How can I evaluate if my model is good by looking at the graph? For example I have this KDE plot of the residuals(it's x_pred-y_pred) of a machine learning evaluation of ...
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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 ...
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