Questions tagged [regression]

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

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Correcting for one of multiple strong batch effects in a dataset

I am wondering which statistical tools to use when analysing data that have multiple strong batch effects (distributions vary from one batch to another). I would like to correct batch effect when it ...
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When using Absolute Error in Gradient Descent, how to calculate the derivative?

What is the derivative of the Loss Function (Absolute Error) with respect to the feature weights that is used to update the weights? Couldn't find anything specific about it anywhere.
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How to choose the suitable Neural Network Architecture for Regression Tasks

I'm working on a project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration, etc. The data that I have comes also with a timestamp for each sample (I mean that ...
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kNN vs Logistic Regression

Good day, I had this question set as optional homework and wanted to ask for some input. Suppose an individual was to take a data set, divide it in half into training and test data sets and then ...
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Threshold for overfitted models

It's common knowledge in DS that overfitted models perform well on training data and poorly on test data. But how do you decide if a model is really overfitting? I have nowhere (books, online courses, ...
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Getting monthly revenue predictions for outlets

I am often presented with a task of predicting monthly revenues of retail outlets. Say I have a training set of N outlets, each associated with a series of historical monthly revenues (target) and a ...
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How to train regression models on imbalance dataset?

I am trying to train a regression model on the imbalance dataset. For example, the input features are vehicle_type, ...
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Using CNN for regression

I have a question about using CNN for regression. My problem is the following. I have a set of coordinates, which represent positions of goods in a warehouse that have to be picked: pick point=(x,y). ...
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How to find bias for perceptron algorithm?

My question is very basic. I am starting with ML and am working on the perceptron algorithm. I successfully computed the weights for this input data: ...
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Multi Output Regression Graph

I am doing a multi output regression using Keras/Tensorflow. I basically have a column of inputs and 2 columns of output points on excel file. I want to fit a curve for that points. The code that i am ...
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Multi-Output Regression with Keras

I am trying to do a multi output regression using tensorflow. I have got a dataset in Excel which includes a column of input points and 2 columns of output. I coverted all numbers to numpy objects. ...
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How can I predict geographical neighborhoods in a city by coords and income

I have a dataset of households in a city with family income and latitude and longitude. I would like predict virtual zones or neighborhoods with boundaries, grouping close families with similar income....
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What is the best approach to train a multi-category regression model?

What is the best approach to train a multi-category regression model (using XBoost decision trees ensemble)? What are the ups and downs of each one? For example, if I want to train a model to predict ...
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Minimum Possible Test MSE

I have a little confusion. What follows is from Introduction to Statistical Learning (2013) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. My understanding of what is going ...
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Bayes factor values

I have two samples A and B with continuous values such as results students. I want to calculate the baysian t test to get the Bayes values. I get the Bayes value of 0.16,so does it mean the null ...
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Same coefficient in multivariate regression with dummy variables

Hello Data Science community, I have a model with 1 quantitative variable (y) and 2 categorical variables. In order to work with the categorical variables I have created dummy variables (binary) for ...
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Quantile Regression is inconsistent (lower quantiles predicting higher values at times)

While using scikit-learn's GradientBoostingRegressor's "quantile" loss, I noticed that when I try different values of q to fit the data at 0.05 (5%) increments, there are instances when predicting a ...
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Mathematics behind multivariate regression

I have sought some help and trained a regression model that takes in a single dependent variable Y and gives the three independent variable R, B and G as output. This has been done in attempt to ...
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Prediction interval and probability to increase

Let be $y_i$ some observed values of a given time-series. We denote $\hat{y}_i$ the corresponding predicted values. We also assume to have a prediction interval $p_i$ such that $\hat{y}_i \in p_i = [\...
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Is there a machine learning method to rank customers credibilty (goodness of a customer)?

I am working on a machine learning project that I want to rank each customer and put on a scale smt like one of those https://cdn1.vectorstock.com/i/1000x1000/90/40/credit-score-indicators-with-color-...
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Classification vs Regression Model what should I choose?

I am working on a problem like 'customers next month revenue prediction'. Here revenue will be the target variable. Again we actually segment the customers based on there revenue(like if they give ...
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Checking if ML model is possible

How can I check if a machine learning model is feasible on a given dataset? What techniques like EDA, correlation etc. can be used to judge if a model is possible i.e. data and predictor variables ...
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sklearn: sklearn.linear_model.HuberRegressor vs sklearn.linear_model.ElasticNet

I am experimenting different loss functions for my regression model. I noticed that in the sklearn, there are: sklearn.linear_model.HuberRegressor and sklearn.linear_model.ElasticNet To me, both use ...
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Nan in target variables Neural Network

Is it possible to train on a dataset with some nan in the target variables? I imagine a sort of loss calculation only for the given target data. Is this Doable in Tensorflow/Keras =?
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Could the Input shape of the LSTM layer not be a constant?

I am trying a vanilla LSTM on my dataset. According to this course; the LSTM layer should be built as follows: ...
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Which Technique should we use for predicting an integer output?

I'm working on a problem where my target feature of type integer. i.e (n_clicks). In general, if we want to predict categorical target feature then we use classification algorithms and on the other ...
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why multiplication (squares) doesn't work for neural networks?

Below code creates sum of 2 random numbers and then we train for 1000 examples and then we are able to predict which works fine consider the below code for creating random data : ...
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Multi-Output Regression with neural network in Keras

I have got an .xlsx Excel file with an input an 2 output columns. And there are some coordinates and outputs in that file such as: x= 10 y1=15 y2=20 x= 20 y1=14 y2=22 ... I am trying to do that ...
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Getting better image regression for new samples

I'm trying to embed the environment of a room into a network; I feed the network with a viewpoint and reconstruct the corresponding image. However, data that the network never sees is reconstructed as ...
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What if there is huge difference between train data and test data?

I trained a model which does well on unseen data,but after deployed on production the data I got is very different,like the highest values in train & test data is ~23,but the data I got from ...
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R2 40% smaller after removing outliers

After removing several outliers from two variables (exactly 15 outliers) R2 score mojego XGBoosta odniżyła z 93.8% do 49.5%. XGBoost turned out to be the best model for my problem (selected on the ...
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MultiOutput Regression Model

I have a dataset of 187 data points (numeric data) with 8 features that I need to train to predict 4 target variables. What would be a good algorithm to go about solving this? Ideally, I want an ...
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how should I train a model with transformed parameters

Lets say that we have the following model: $y = sigmoid(w1)*x1 + w2*x3 + w3*x3$ and we would like to fit it in a dataset $(xn, yn)$. How can we do this? (e.g using gradient descent? Also, what could ...
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Regression Trees - Splitting and decision rules

I understand that a regression tree is built by splitting a node, such that the MSE for the label/output variable is minimized in each of the two resulting nodes. I have two questions about this: 1.)...
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Feature selection, is it possible to combine wrapper and embedded methods?

I'm using neural network to predict PM10 concentration (a regression problem). Since the wrapper method is dependent on the model, so passing the neural network model that's optimized for all the ...
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different outcome of feature importance and coefficient from same data

I built a regression model to predict profit based on client, sales person, product category, client industry and client region. After trying several models with tuning hyperparameters, I found that ...
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training data in supervised models, case of linear regresion

I have a newbie question about the case of linear regression or other supervised models for prediction. Imagine that I have the following dataset represented by the X array: So we have n observations ...
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In regression, is a higher adjusted R-Squared ALWAYS better?

Bit of a strange question...I'm trying to put together a multiple regression model. I have daily temperature as one of my control variables. I really want to control for temperature, but ...
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XGBoost: # rounds is equal to n_estimators?

I'm running a regression XGBoost model and trying to prevent over-fitting by watching the train and test error using this code: ...
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Fitting surface using Gaussian Processes (GPs) enclosing 3D cloud

I have a 3D cloud of data points representing an arbitrary shaped volume. I want to fit a GP to the outer surface of this 3d cloud. I saw many examples for interpolation using GPs. They only speak ...
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regression problem predicts a constant, where most of the inputs are the same

I've got a regression problem where most of the inputs are the same (99 out of 100) and the last input gives the variable that I want to predict. I want to predict many things given the 99 identical ...
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LASSO remaining features for different penalisation

I am using the sklearn LASSOCV function and I am changing the penalisation parameter in order to adjust the number of features killed off. For example for $\alpha = 0.01$ I have 55 features remaining ...
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Simmultainiously calculting loss from target interdependend metric

Is there a way to incorporate multiple targets into one loss? Currently, I work with the Sequential() API, I guess this won't be sufficient.... I work with area predictions as targets. Each sample ...
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I have transformed my cyclic variables into sin-cos variables; will I need further normalization/standardization?

I have a dataset where there are both numerical, categorical and cyclic(month-quarter) variables. I will run a regression model, but I may also use Random Forest, XGBoost etc. So I will preprocess my ...
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A metric to include both accuracy and time

Problem Statement : Given a signal, predict some property of the signal. Let's say for discussion here that this property is the frequency of the signal. Clearly the output will be a regression value ...
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Multiple linear the final X output is the same as the imported one despite the fact that p-value are bigger than 0.05

I am making a simple test on multiple linear regression. Importing datasets and libraries ...
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Fit to a power law

I often encounter data which I hypothesize to be from a shifted power law, $ y(x) = A x^k + B$. I have in mind samples from an unknown deterministic function here, but you can think about a ...
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What regression model can handle tiny amounts of data?

I'm trying to use machine learning to predict properties of a material during a crash test, but each data point requires physically crashing an expensive toy car, so I can only gather a few hundred ...
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Modeling the Price Movement- What analysis should be used

I am trying to model the price of a hotel as the check-in date arrives. I have a data set which looks like- For e.g- if I am looking at the booking date of Dec 31st, I would want to analyze the ...