Questions tagged [regression]

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

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15 views

Can LSTM be used to predict value as regression problem?

I have time-series data as shown below. Which model is generally preferred if grig_id is needed to be predicted? Is it possible to use LSTM with a sigmoid ...
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26 views

How to deal with the difference in the range of Dependent variable in Train and Test data

I am training a XGBoost model for predicting number of applications and the minimum number of applications in training data is 40 and the maximum number of applications is 2000, while in test set ...
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29 views

How to compute time-lagged correlation between two variables with many examples at each time t?

I have a dictionary of following form: datetimes = {year : {name : (score1, score2)}} #there are 50+ names/year So, essentially, I'm trying to get an aggregate ...
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59 views

How to work with input which is a combination of metadata+ vectorized text data + image pixel data to build a Regression Model (predict views)?

There are 4 datasets (all in csv format), each has a uniqueID column by which each record can be identified. Image and text datasets are dense datasets.(need to be converted to ndarray). Can someone ...
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20 views

CNN-Regression with a variable number of outputs

I want to predict several variables describing an object on an image. I can use CNN Regression to do that. But how can I do that when the number of objects on the image differs from one image to ...
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25 views

Is it possible to use Memory Augmented Neural Network for regression?

Now I am working on stock price prediction using Machine Learning. I have tried different methods (ARMA, LSTM, GRU). Now I would like to try MANN (Memory Augmented Neural Network). I have tried to ...
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1answer
55 views

What method/algorithm for constrained multi-target regression

I am working with three dimensional measurement data and want to model them using a multivariate linear regression. I have already implemented a simple gradient descent algorithm to solve the classic ...
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18 views

Understanding Standard Error of Regression Coeficients (ISLR)

During my reading of Introduction To Statistical Learning, I reached this part and I'm facing a very hard time trying to understand what is being said: This highlighted words are telling that this is ...
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49 views

Time series forecasting. How use future values

I have a time series dataset containing hourly data from a few year, like below. Let's assume that I want to make prediction for the next 3 hours (2021-01-01 19:00, 2021-01-01 20:00, 2021-01-01 21:00)....
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1answer
18 views

Are there readily available models that can handle conditional correlation?

I've been working my way through the features of the Kaggle House Prices dataset (Note: this is a non-ranking entry, so this is just for exercises), and I've found a couple situations where there is a ...
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1answer
177 views

How to interpret the Mean squared error value in a regression model?

I'm working on a simple linear regression model to predict 'Label' based on 'feature'. The two variables seems to be highly correlate corr=0.99. After splitting the data sample for to training and ...
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1answer
41 views

Doing a cox regression, how do I analyze a continuous variable in which a lower result is worse?

I have this variable that is clinically worse the lower it is. Instead of interpreting, for example, a hazard ratio of 0.9 as "per unit increase in variable x, there is a x% reduction in risk for ...
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221 views

Cast regression problem into classification problem

In the TA session, my TA claimed, that regression problems should often be cast into classification problems by dividing the output range into bins and then using a multi-loss, since we have better ...
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13 views

One LSTM for two currencies or two LSTM one for each currency?

Suppose I am building an LSTM model for currency forecasting. Assume that I am working on two rates: USD vs GBP and USD vs EUR. Should I build one LSTM model with input size of two features (GBP and ...
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1answer
25 views

RF regressor for probabilites

I am using sklearn multioutput RF regressor to learn statistics in my data. So my target contains several probabilities for the different features, and the sum of all these probabilities is one as ...
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1answer
66 views

How does the equation "dW = - (2 * (X^T ).dot(Y - Y_hat)) / m" comes in Linear Regression (using Matrix + Gradient Descent)?

I was trying to code the Linear Regression in Python using Matrix Multiplication method using Gradient Descent and followed a code where there was no mention what is the loss but just a code as Per ...
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97 views

SelectKBest for regression `f_regression` behaves weird when changing the random_state parameter when splitting

I am working on a regression project using the Audi dataset from Kaggle. I have looked at other notebooks and i saw that people use SelectKbest. I tried using the same thing, but when I was splitting ...
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2answers
34 views

Multiple Regression, Classification and Boundary Poins

I have two gangs which are doing crimes. And i want to classify them. Lets say I'm looking for a regression function: M(x1, x2) = w1x1 + w2x2 + w3 Now I have ...
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1answer
15 views

Material Science dataset with feature-dependent inputs

I'm dealing with a material science/chemistry dataset where I have a bunch of duplicates inputs formulas corresponding to different values of a specific features like temperature. It looks something ...
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1answer
39 views

Re-training regression model on covid data [closed]

I am trying to re-train a regression model (XGB regressor) which was used in the pre-covid times (Feb 2020). The dependent variable for the model is the number of bookings done, and due to covid, the ...
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28 views

Variance of prediction doesn't match input data with Keras model

I'm using Keras to do a regression on inputs. I've tried a lot of different models, and a lot of them converge around the same place. My problem is with the distributions of the results. In order to ...
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39 views

What can I infer from a linear correlation of regression coefficients?

I am working on a dataset for classification where each observation is a series of values of a certain measurement $Y$ for a fixed range of values of measurement $X$ (i.e. a discrete mapping from $A \...
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49 views

Use Machine Learning/Neural Network + Distance Measurements to Find the Position of Devices (Localization)

I want to find the position of several devices using at least distance measurements. These measurements are done using a radio, and it might be that not all devices are in radio range (no distance ...
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1answer
59 views

Feature importance of random forests

I have a dataset with 11 features, I noticed that manipulating these features (eg dropping one or some of them) doesn't affect the error scores of training and testing data, so I had to check the ...
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1answer
38 views

Find the number of homes with 3000 sq ft , 3 number of bedrooms and 40 years of age?

there is a dataset like this area bedroom age price 2600 3 20 550000 3000 4 15 565000 ... Now the questions are find the price of the ...
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76 views

How do you choose a kernel for a discontinuous function in Gaussian Process Regression? [closed]

I'm doing Gaussian Process Regression and created a series of functions by gluing other functions together on random places. Here's an example: Perhaps this one is to complicated, but all the ...
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59 views

How to process categorical variable having lots of unique values in linear regression?

I have House Price dataset and I am using linear regression to predict the house price. while data preprocessing I found a variable called "Location" and it have around 342 unique value. For ...
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1answer
16 views

Neural network type question

This web link is to a site that talks about forecasting building electricity, like a time series regression concept. In the article they talk about the NN architecture as: the architecture of this ...
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2answers
63 views

How close is close enough, with regression?

When exploring different techniques in machine learning (neural networks), I like to use binary classification problems as a test-bed, because it's very easy to understand how well the technique is ...
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32 views

Designing a network for multiclass regression

I'd like to model a continuous conditional probability distribution for two classes on a given data set. eg the height of men and women from a set of inputs. I can train a regression model (DNN, CNN, ...
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3answers
54 views

How to decrease $R^2$ value and change it to positive value [closed]

I'm working on a data, and use regression , as you see bellow: from sklearn.svm import SVR regressor = SVR(kernel = 'linear') regressor.fit(trainX,trainY) above ...
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1answer
25 views

Performing anomalie detection on a battery volatge using LSTM-RNN

I am trying to detect anomalies in a battery output voltage for one month. I have the next data frame, as it is shown the data is collected each minute for each day so I have almost 1420 sample per ...
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51 views

Is it valid to add MAPE as a margin to prediction output?

I've trained a KNNRegressor on predicting used car prices. A given car's actual selling price is R289,995. My model predicts R260,911. I want to be able to tell the user My knn model predicts the deal ...
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11 views

Ordinal Regression to understand Google Rankings

I've made a dataset of search engine rankings for web pages versus a host of on-page factors (such as the amount of words on the page or the lenth of the tag) and I would like to try and build a ...
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1answer
28 views

Regression analysis and least square method relation? [closed]

I want to know where Regression analysis is most used at, what's its competitor methods, and how least square method relates to regression analysis.
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2answers
23 views

ResourceExhaustedError when building Sequential model

i have a big problem when trying to build my model, ...
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1answer
17 views

Sum of squares for matrix valued data over $\mathbb{R}$ and $\mathbb{C}$

Let us assume we have $k \times k$ matrix valued data and assume this is organized (possibly as time series): $$ M_1, M_2, \ldots, M_n $$ Now, assume we are interested in writing down an error ...
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9 views

How to determine activation functions for neural network

I am trying to plan a neural network for regression predictions. The final activation layer should be a linear function, but for hidden layers, do the activation functions need to also be all linear ...
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1answer
24 views

Treating continuous data as a classification problem by predicting bins or quintiles

I currently have a model that has several numeric Y or predicted variables Sample Data: Y1 Y2 ... YN 2710 0.32 ... 31231 1710 0.52 ... 51231 I am currently using regression (multioutput regression ...
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2answers
53 views

Machine Learning algorithms and Cross Validation, the best practice

I'm new in Machine Learning, and I'm studying the main concepts behind algorithms from the mathematical point of view. I'm also trying to start implementing some algorithms for regression purposes ...
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2answers
95 views

Selecting most important features for multilinear regression

I have a set of 25 features. I would like to choose the best features for my model. Originally, I was looking at the correlation of features with respect to response, and only taking those which are ...
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90 views

Keras custom metric doesn't work as loss function [closed]

Referencing my previous question here. I've managed to get my angular error metric working with tf.py_function; ...
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0answers
19 views

Correlation Study to Determine Weights Of Fields

I have several input fields, and the content for each field can either be correct or incorrect. These fields are then sent to a black-boxed function (which I can’t control), and the output of the ...
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19 views

Normal distribution of errors

I'm trying to project lifetime of customers in my company, based on various parameters I've reached a 64% correlation so far, between the valid and prediction data I'm using light GBM regressor I did ...
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1answer
96 views

Hyperparameter tuning with Bayesian-Optimization

I'm using LightGBM for the regression problem and here is my code. ...
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5 views

How to explain ANN can predict much larger output values (e.g., y>2.5) when it was only trained with small output values (y>=2.5)

I have trained models with both ANN and XGBoost. I am wondering that whether ANN has the ability to predict much larger output values (e.g., $y>2.5)$ when it was only trained with small output ...
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1answer
40 views

Which data science model is best for explainability for prediction problems?

Imagine you have to create a model to explain to stakeholders e.g. to predict price, weight, sales etc.. Which regression models offer the best in terms of explainability and interprability? ... Which ...
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1answer
54 views

Lasso regression not getting better without random features

First of all, I'm new to lasso regression, so sorry if this feels stupid. I'm trying to build a regression model and wanted to use lasso regression for feature selection as I have quite a few features ...
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9 views

Modeling a time series quantity by modeling its constituent time series

I have a time series target, let's say $Y_1$. This quantity depends on two other time-series quantities deterministically, $Y_2 \text{ and } Y_3$. That is, we have some function which takes $Y_2$ and $...
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25 views

VGG16 based model not learning to recognize emotions from videos

My model looks like this ...

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