# Questions tagged [regression]

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

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### Neural-Networks - preferred method for training, classification v.s. regression

As a conclusion of their paper "Efficient Backprop" (http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) (§10 Discussion and Conclusion), LeCun and others conlude that the preferred method for ...
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### Why does reducing polynomial regression to linear regression work?

Getting into machine learning, have a reasonable background in statistics and understand the basic principles of linear algebra (matrix multiplication etc.) - but am having a damn hard time figuring ...
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### Compare Coefficients of Different Regression Models

in my project, I am using asuite of shallow and deep learning models in order to see which has the best performance on my data. However, in the pool of shallow machine learning models, I want to be ...
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### variables selection in regression models

I develop price prediction data model using multiple linear regression, ridge, lasso and elastic net regression, initially I had 215 variables. after creating models I ran a python code to check how ...
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### Confidence intervals in multivariate linear regression

I am fitting my data to a multivariate linear regression $Y = BX + \Xi$, where the response is bivariate $Y\in R^{n\times 2}$, and the predictor is uni-variate but elevated to the projective plane to ...
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### Tensorflow - simple multi-layer perceptron not stabilizing around mean of normally distributed y-values

I'm building an FX trading model where I'm trying to predict the +/- movement of a currency pair 5 minutes into the future. I've had some promising results adapting the model as a classifier (i.e., ...
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### How can I improve my non linear multi dimensional regression? 8 parameters and 8 sets of data, 4 variables

I have this system of equations that I need to fit 8 parameters with 8/maybe 9 sets of experimental data: My response variable is [DCF], I have three independent variables: [O_3], [FeOOH] and time. ...
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### Calculate a Rank function from Regression features

I am using 3 features (x1, x2, x3) for regression. Some of my features are continuous some are categorical. My dependent variable are lets number of bookings. And I can predict the number of bookings....
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### Get the Polynomial Equation with Two Variables in Python

TL;DR predict "price", given "length" and "wandRate" I have some time-series data where the dependent variable is a polynomial result of 2 independent data points. Here is a snippet: This is ...
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### how to know what my data are uninformative and that machine learning will not work with it?

following this question, I'm making a data analysis again because I tried to use machine learning algorithms like Random forest to predict a value from certain features but it didn't work for me. I ...
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### Loss function for multivariate regression where relationship between outputs matters

I am attempting to build a sequential model with Keras (Tensorflow backend) that has multiple outputs. My targets are proportions of a whole so each observation is an array like [0.5, 0.25, 0.15, 0.1]....
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### How to pass linear regression weights to Xgboost regressor?

I'm trying to build an xgboost regressor or a catboost regressor for a task. I have a working linear regression model. I also trained an xgboost regressor model for the task but it was worse than the ...
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### How to predict the dealer whether pick up the goods next month?

Here, I want to predict dealers(about 600) whether pick up the goods(about 30) next month. As you see, there are about 18,000 possibilities and it's difficult to predict. By the way, now I have ...
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### Which kNN model to chose?

I am trying to tune the "n_neighbors" for a kNN model andI have the following problem : Based on the mean cross validation score the optimal kNN model should be the one with 10 neighbors. On the ...
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### why does making the target variable normally distributed helps?

while working on some regression problems I have found that if the target variable is skewed, making it normally distributed(using transformations) almost always helps. Why is that? Should we also ...
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### Handling data with exactly same features but slightly different outputs? [Regression Problem]

I have a dataset where if I remove two attributes, in some cases remaining features are exactly the same but target value changes slightly. In my test set, I don't have those removed attributes so I ...
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### LSTM with linear activation function

I'm trying to do multi-step regression and I use an output layer: LSTM(1, activation='linear', return_sequences=True) Is this the wrong way of achieving this? Should I use a TimeDistributed(Dense(1))...
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### Upper bound on 'relatedness'?

We have ~100 answers to a questionnaire with five questions (Q5). Independently from that, we have about 50, somewhat overlapping, features describing the people who answers the questions (F50). After ...
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### Need help with method=“leapSeq”

Here is my code: ...
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### ARIMA: How to understand performance of the model?

I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model... Here is what the ...
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### Curve fitting through cloud of daily values using machine learning

I want to plot laboratory values (SCr_v) over time and find the best fitting regression curves (see plot 1 and 2). I don't want to restrict myself to a specific model if possible. Is there a function ...
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### Are stationarity and low autocorrelation the prerequisite of regression model?

As said in the title, are stationarity and low autocorrelation the prerequisite of general / linear regression model ? That is, if a time series is non-stationary or has large autocorrelation, would ...
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### what should I do if my Neural network model stuck on high value loss?

I'm using neural nets in my projects. It's a regression problem where i have 3 features and I'm trying to predict one continuous value. I noticed that my neural net start learning good but after 10 ...