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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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How to measure correlation between several categorical features and a numerical label in Python?

I have for a few weeks measured the time it takes for a product to be released through a automated release pipeline. I have several different categorical features such as "Product Category", "Product ...
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Prediticting global solar irradiance using satellite images

I have the aim to build a model to predict global horizontal irradiance (ghi) using satellite images and other features namely the day of the year and time of the day. For extracting the satellite ...
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1answer
11 views

In a residuals vs fitted plot, how do I interpret a homoscedastic variance that is not randomly distributed above/below the line?

I'm learning linear regression, and I ran a step function for linear regression and checked out the residuals vs fitted plot for the final equation. The residual looks homoscedastic but it's not ...
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2answers
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Regression using gradiant boosting - smoother predictions

I have practical machine learning problem. I have trained a LightGBM model to predict house prices. Compared with other models I have tried, the loss (RMSE) is quite low and overall I'm quite happy ...
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Why I am getting prediction score 1 i.e. 100%

I am reading few parameters and trying to predict target value using Linear regression and GB. Surpicingly I am getting score = 1 on test data. How come? Can anyone tell me whats wrong with this code? ...
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Regression model Giving the same prediction for all new inputs until i load the model again

I have build a regression model that has some decent accuracy measures. I have pickled it and loading it another project. However it is producing the same predictions every time when i pass new ...
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1answer
50 views

ML regression poor performance

I am experimenting with 3 years time series electrical demand data (kW) for a building and attempting to create regression supervised ML models from sci kit learn regressor algorithms but I have very ...
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1answer
109 views
+50

Understanding output of LSTM for regression

I am working with embeddings and wanted to see how feasible it is to predict some scores attached to some sequences of words. The details of the scores are not important. ...
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1answer
9 views

How do we go about imbalanced data for prediction problem? [duplicate]

As in classification we have imbalanced classes, we use up-sampling or down-sampling and other techniques, what do we do when we have imbalanced data in prediction problems, for example, I have ...
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1answer
66 views

Does Gradient Boosting detect non-linear relationships?

I wish to train some data using the the Gradient Boosting Regressor of Scikit-Learn. My questions are: 1) Is the algorithm able to capture non-linear relationships? For example, in the case of y=x^2,...
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Imputing ecological time series data based on known other data

I am working on a research project where I have missing daily data in one variable, Sea Surface Temperature (SST), from 1968-1981. I understand this is a lot of data to impute, but I can't afford loss ...
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1answer
38 views

Loss Function for Probability Regression

I'm trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
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1answer
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Intuitive Explanation of R-squared

Here is a nice definition of R-squared that I have found on the internet. R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the ...
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1answer
35 views

unsupported operand type(s) for -: 'list' and 'list' using python

Here I have a data file and I designed neural network to predict value. I have a three inputs. These three inputs affect to predict value bysubtarcting and adding. If my three inputs are x1,x2,x3 . X1 ...
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1answer
34 views

How to start building a statistical regression analysis model with multiple categorical/discrete input variables of high dimension in Python

I'm fairly new to data science and ML. I have data of an item going through a release process. I have collected data on various variables such as "product category", "product line", "design country", "...
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1answer
23 views

Regression - Unbalanced Categorical Features

I have a data set that has some unbalanced categorical features. I would like to build a regression model to predict a label using machine learning (ML). How do I handle data imbalances in ...
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14 views

Images Score Regression only regresses to the average of the target values

I have 700 3D images, each one having a target value. The target value distribution after standardizing looks as below After training, my validation set MSE (10% of data) does not go down and R2 ...
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Tuning a sequence to sequence model

I have written a variable length sequence to seqeunce autoencoder in keras using this tutorial as a guideline: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras....
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Regression with -1,1 target range - Should we use a tanh activation in the last 1 unit dense layer?

Say in a regression problem the target range to be between [0,1] or [-1,1], and say the last layer of the network is as ...
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ML algorithms for regression in the case of label noise with a known distribution?

I'm pretty new to machine learning, and I am interested in some ideas for algorithms or references for papers for using a ML algorithm for regression when the labeled data has label noise with a known ...
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11 views

Why do we reduce magnitude of the coefficient in regression

Why do we reduce the magnitude of the coefficient in regression? how does it help the model?
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1answer
49 views

predict future value in every one hour using (t+60 minutes) LSTM neural network in python

I have a data csv file including with three inputs and two output with time series. Here data took an every one hour one hour. So I need to predict my next future value at t+60 according to the ...
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33 views

XGBoost regression

I run XGBoost regression with tree as base learner. I have over 400 variables and more than 30000000 samples. I have generated most important features and was surprised to see that one feature is ...
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Regression loss function is nan

I'm a beginner with ANN and DL in general. I have a regression task with a target of 2-dimensions, my dataset only have 46 samples (small dataset I think). I tried the code below that does a ...
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1answer
20 views

Predicting service date

If I had an automated system that pays my bills, but the website where I pay them won't tell me when the next bill will be available. What is a good approach of predicting the date of the next bill? I ...
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1answer
74 views

What is the difference between regplot and lmplot in seaborn?

Seaborn library in python suggests to use either lmplot or regplot to visualise a regression between two variables. What is the difference between the two plots ? The result I was able to get are ...
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Need Weibull and Gamma Regression Data

I am working on regression with non-normal responses. Now i need a data set(s) in which responses follow Weibull and Gamma distribution, with some reference. kindly data may contain at least 15 ...
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1answer
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Dealing with normalized regression output

I have a regression model that is trained on a bunch of features and normalized targets so naturally when I use the model to predict on a new input, the output is also normalized (well not normalized ...
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2answers
78 views

Are RNN or LSTM appropriate Neural Networks approaches for multivariate time-series regression?

Dear Data Science community, For a small project, I've started working on Neural networks as a regression tool, but I am still confused about possibilities of some variants. Here's what I am aiming ...
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2answers
43 views

Choosing between Regression and Classification

I want to build a model using a neural network that will be able to extract some features from landscape pictures. In order to improve the efficiency of my model, I first want to extract the "...
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22 views

ValueError: Number of targets and number of penalties do not correspond: 100 != 1

I am getting this error after running the below code, what am i doing wrong? ...
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3answers
61 views

How to understand features impact in a non linear case?

I give a simple example: I have a set of houses with different features (# rooms, perimeter, # neighbours, etc...), almost 15, and a price value for each house. The features are also quite correlated (...
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2answers
34 views

How can someone avoid over fitting or data leak in ridge and lasso regression when the training score is high and test score is low?

I used the code provided here: https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b The only difference is that i used StandardScalar on my ...
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1answer
42 views

Mean Absolute Error in Random Forest Regression

I am new to the whole ML scene and am trying to resolve the Allstate Kaggle challenge to get a better feeling for the Random Forest Regression technique. The challenge is evaluated based on the MAE ...
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1answer
47 views

Architecture for linear regression with variable input where each input is n-sized one-hot encoded

I am relatively new to deep learning (got some experience with CNNs in PyTorch), and I am not sure how to tackle the following idea. I want to parse a sentence, e.g. I like trees., one-hot encoded the ...
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20 views

Which loss function to use for predicting traffic vehicle count?

I want to predict the traffic vehicle count of different junctions in a city. Right now, I am modelling this problem as a regression problem. So, I am scaling the traffic volume (i.e count of vehicles)...
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Multi-Class Classification With Logistic Regression On Binary Data

I am trying to implement a multi-class classifier with using logistic regression. In my dataset, attributes are words, for example first attribute is 1 if the data instance includes word "x" and it is ...
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2answers
80 views

Validation loss is not decreasing

I am trying to train a LSTM model. Here is train and validation loss graph. Is this model suffering from overfitting problem ?
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1answer
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When is a neural network better “traditional” models like decisions trees and lassos?

There's a whole theory of statistical inference based off calculus studying consistency, efficiency, robustness, BLUE, unbiasedness of linear models (Gaussian,Exponential, Chi-square, F-distribution, ...
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2answers
66 views

Keras neural network entirely different results on two different hardwares (one is AWS)

I have a Keras model. which is defined as follows: ...
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1answer
40 views

Creating a neural network, composed of n times a different network. Is it possible?

I'm currently working on a project with a bunch of data of devices that can either belong to people, or not. The ultimate goal is to estimate a number of people detected. Sadly, it is impossible to ...
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29 views

What would be a suitable method to select the first N best models in keras?

My problem is a regression problem and as a result, the aim of the keras model is to minimize the loss of the predictions. I have a list of hyper-parameters with 100 hyper-parameter and have created ...
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2answers
29 views

Regression for discrete values?

Im a noob in ml / statistical algorithm, but I do have worked with simple classifiers and regression I like some opinions if I am going the right way, given my limited knowledge My problem is ...
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1answer
21 views

my r2_score is negative

My work at college is to estimate the value of some points. So, I need to predict 8 points based in another 8 points. When i run the algorithm, the output values are not even close to the input ...
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2answers
43 views

MAD vs RMSE vs MAE vs MSLE vs R²: When to use which?

In regression problems, you can use various different metrics to check how well your model is doing: Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better Root Mean Squared Error (...
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0answers
51 views

Kriging (Gaussian process regression) vs Neural Networks

How is it that Kriging models outperform neural networks when deriving a model to fit data? I also noticed that Kriging is the most accurate (zero error) at training points and relatively inaccurate ...
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1answer
124 views

How to calculate prediction error in a LSTM keras

I have an LSTM which I have constructed and run in keras using python. I use this model to predict $n$ points into the future for a time series forecasting problem. When I use a method such as ...
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1answer
37 views

Defining Input Shape for Time Series using LSTM in Keras

I have been trying to model Time Series forecast using Keras LSTM algorithm. My dataset consists of weekly sales data from Jan-2016 and I also have external features such as Festivals/Events each ...
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1answer
26 views

How can I avoid requiring global information for performing regression on meter variables?

Note: With a meter variable a timestamped value is the sum of all previous differences plus a difference to the most recent value. Think of a electricity meter counting the use of energy. The goal ...
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3answers
35 views

Should I build a different model for each subset

I have a dataset which has categorical variable class. I am trying to solve a regression problem I am not understanding whether I should build a model on entire dataset and consider variable class as ...