Questions tagged [regression]

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

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6 views

Time Series Forecasting for Yearly Data

I have a project that will be focused on collecting financial data from users (Revenues and Expenses). I want to include and AI solution that can take the data for each user and give them a ...
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Can I compare two models trained on different but similar datasets to help find differences between the two datasets?

I have a multivariate dataset the contains A and B. I want to see if there are differences between the A and B samples. I currently have two ideas on how to do this, but I am not sure if they are ...
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Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?

Normally we would remove features that have high pairwise correlation with another feature before performing regression. But is this step necessary if I am applying L2 regularized logistic regression (...
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Loss function for normal distribution regression problem

My project involves training an input of random uniformly distributed data using regression (this is my approach) to output random normally distributed data. The issue with formulating the problem is ...
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XGB Regression: Is there a way to handle somewhat bimodal Y variable?

I am using XGBRegression to predict on continuous percentage data with 80% of the values around 100, 10% around 0 and 10% data distributed in the middle. Models are struggling with predictions around ...
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Does the appliance of R-squared to non-linear models depends on how we calculate it?

Does the appliance of R-squared to non-linear models depends on how we calculate it? $R^2 = \frac{SS_{exp}}{SS_{tot}}$ is going to be an inadequate measure for non-linear models since an increase of $...
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1answer
24 views

How many features should be there in a dataset to apply any feature selection method?

I am working on a time series, regression problem, where I have 10 features and 180 observations. I would like to understand what the minimum number of features should be in a dataset to use feature ...
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My Models giving negative scores

I am new to Data Science. I am trying to use following dataset in order to predict prices for some reason my models except for decision tree is giving negative score. Please help me to build this ...
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Determining which model result is better

I am trying to determine which model result is better. Both results are trying to achieve the same objective, the only difference is the exact data that is being used. I used ...
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Automate detection of overfitting models based on autoML libraries

I'm trying to use machine learning to impute missing data in series using some auto-ML libraries in python (so far : dabl, FLAML, auto-sklearn and AutoKeras). I know the way to detect overfitting in a ...
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1answer
20 views

One predictor variable and 3 response variable (categorical and continuous) [closed]

If I have predictor variables which are a mixture of continuous and categorical, and a response variable that is continuous. What approach should I apply? Linear regression, logistic regression or k ...
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How to reduce RMS error value in regression analysis & predictions - feature engineering, model selection

There's this dataset containing the metadata of Twitch's top 1,000 streamers of 2020. You can have the details here. I am currently participating in a challenge to predict the values for Followers ...
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Which algorithm works well for forecasting sales prediction and the reason to choose particular algorithm?

I am working on a project 'Rossmann Sales prediction', in which I have to forecast the sales of Rossmann Stores. So it is a supervised ML problem. I applied random forest. But then in interviews ...
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How to model a arrival process with increasing features?

Suppose a website records all information related to visits including gender, device, time, etc. When a new impression happens we store it and we want to predict when this person will re-visit the ...
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31 views

Predict apartment prices with two sources of prices

I am asking for help with the following problem. There are two subsamples in the dataset - one where the target is real(valid), and the other where it is approximate (I do not know how it differs yet, ...
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Best Approach for Predicting NFL Betting Outcomes

I play a game every year with my family, where we compete to make picks against the vegas odds for each NFL game. We aren't actually betting any money, but instead we each try to make the most correct ...
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The effect of the λ in the Ridge regression

Why by increasing value of λ in Ridge estimator the slope of the line is decreasing? How exactly λ affects to the y = kx + b?
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How to improve regression neural network?

I am new to deep learning and data science and trying to increase my knowledge by working on some hackathons. Currently, the hackathon project I am working on has the task to predict the closing price ...
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Automatic detection of ML problem type: Regression or Classification

I am trying to design an algorithm that based on training data automatically detects ML problem type: Regression or Classification. There is no need to say that it is impossible to design such an ...
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RANSAC and R2, why the r2 score is negative?

I was experimenting with curve_fit, RANSAC and stuff trying to learn the basics and there is one thing I don´t understand. Why is R2 score negative here? ...
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FFNN vs. RNN for Regressing Physical Sensor Timeseries Data

I'm trying to build a network to regress data from one sensor to another. The target sensor is a scalar time series and the feature sensor can be either a scalar or vector time series. Both timeseries ...
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Simplest NN regression model for artificial 'rectangular' pattern?

Asuming we are looking for a simplest Tensorflow regression model for nonlinear dataset (1,) -> (1,) (a 'rectangular' pattern): This example dataset has 10000 ...
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What approach should I take if my feature value changes after the initial prediction?

Goal - Predict number of days the finished good would be delayed from a promised date of delivery? Background - It is only 7 weeks before the promised date of delivery that the demand becomes proxy ...
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Classification for Ordinal labels - what tree-based methds can i use?

I have a label that has a natural ordering e.g. 0,1,2,3 where 0 is the worst activity measure and 3 is the best. For each label given by the model i need to also give the probability that it belongs ...
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Prediciting multiple target variables - multioutput regression

I am trying to predict how often a new patient will have specific treatments. The target values are 8 different treatments and the independent variables are age, sex, etc. The outcome of my prediction ...
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Encoding "histogram bins"

I am currently working on a regression problem where I have one variable (x) of the data in the form of "histogram bins". I.e. I could have value ranges ...
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1answer
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How to set coefficient limit in lasso regression in Python?

I'm working on a regression problem where I want to use Lasso model. With the help of Lasso and LassoCV, we can provide different alpha values and get the best parameter and coefficients however I ...
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Bounded regression problem: sigmoid, hard sigmoid or…?

I have been training a neural network for a bounded regression and I am still in doubt for which activation function to use on the output layer. At first, I was convinced that a sigmoid would be the ...
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RMSE vs R-squared

Question: Which is a better metric to compare different models RMSE or R-squared ? I searched a bit usually all the blogs say both metrics explain a different idea, R-squared is a measure of how much ...
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Constraining linear regressor parameters in scikit-learn?

I'm using sklearn.linear_model.Ridge to use ridge regression to extract the coefficients of a polynomial. However, some of the coefficients have physical ...
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1answer
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Regression prediction for HVAC unit Best way to utilize available data?

I am starting to investigate machine learning applications for HVAC at the commercial level. I am an HVAC controls person by trade that has recently taken some basic courses on Machine learning and ...
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1answer
58 views

Multi-target regression tree with additional constraint

I have a regression problem where I need to predict three dependent variables ($y$) based on a set of independent variables ($x$): $$ (y_1,y_2,y_3) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \...
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1answer
44 views

Turning regression problem into "classification + regression"

Say I have a regression problem where I'd like to predict values ranging from 0 - 100.000 based on some predictors. A single XGBoost model achieves decent overall mean performance (measured by MAE) ...
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How to model multinomail logistic regression to get desired outcome?

I want to run multinomial logistic regression in SPSS. Dependent variable: Code=0 Cognitively normal(CN) Code=1 Mild cognitive normal(MCI) Code=3 Alzheimer’s(AD) Independent variable: Genotype.1=0 ...
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Multivariate regression - not enough data?

I have a table with data about 10 agriculture parcels. Each parcel has data in time regard the number of nutrients each parcel has received in each day and in the end I have the total number of ...
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Predict a continuous data without a linear shape on data points

I have a dataset like that I want to predict the financial loss given the incident type. This is a brief visualisation As the financial loss is a continuous data, I know that I cant do a linear ...
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Aren't balanced data sets important in regression?

Why is it that the necessity for balanced data sets is (almost) always exclusively mentioned in the context of classification but not of regression?
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OLS Regression: Predicting to the certain total

I have a simple dataset: Rooms Price(in March) Single 20 Balcony 50 Triple 100 Couple 75 Family 150 Now, I can predict the values and set the ...
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How do i perform multinomial regression ? I have to classify students based on the program they are enrolled in. Please give me some ideas or hints

Things I have tried. Used dummy variable df=pd.get_dummies(data = df, columns=['Gender', 'ses', 'schtyp', 'prog'],drop_first= True) Then used test and train But ...
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27 views

How to consider the change in categorical variable in multiple linear regression?

I am building a multiple linear regression model to predict the mileage of tires and one of the independent variables is the wheel position. It is categorical and I could encode it to run the model ...
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How does regularization work?

Could any explain how regularization works with the noise point. Like in this image, the $2^{nd}$ graph is where the decision surface is good, where the noise points are close to the actual points.
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1answer
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Trying to compress text with NLP

For a university project, I need to send text in Spanish via SMS. As these have a cost, I am trying to compress this text in an inefficient way. This consists of first generating a permutation of ...
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Like Time-To-Event analysis, but looking at the timing of events that do or do not happen on a binary outcome

I have a problem where every observation has a binary outcome that occurs at the end of a fixed period, and the predictor variables describe a few types of event that either happen on some day within ...
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Why are the values of my Y predicted the same and R-Squared Negative in SupervisedDBNRegression, Neural Networks

My model is not outputting the results I expected. I don't quite know my way around ANN. After learning how to use SupervisedDBNClassification from https://github.com/albertbup/deep-belief-network I ...
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multi linear regression result interpretation on variable squared

I have trouble interpreting the variable squared result on this regression. for the information, my dependent variable is the gallons consumed by the aircraft. The Leg mileage variable is the distance ...
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How can - and why does - Random Forest over-forecast? [closed]

My understanding of Random Forest Regression is that each leaf node contains one or multiple examples from the training data. When predicting, each tree finds the most appropriate leaf and takes the ...
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25 views

inbuilt python module for regression of multivariate

I am working on the following problem: In linear regression, I have used the python sklearn.linear_model LinearRegression by calling ...
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1answer
14 views

Logistic Regression Model on Test Set - in Titanic Data Showing Error

I have built the model on Titanic Data set , with Logistic Regression Succeffullly and it is giving prediction on training set , but unfortunately I am unable to implement this on test data set. ...
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Random forest regression model improvement

I am working with vehicle occupancy prediction and I am very much new to this, I have used random forest regression to predict the occupancy values. Random forest jupyter notebook have around 48 M ...
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Why is a saved model predicting better on the total data compared to cross validation on training data and predictions on test data?

I’m using random forest in orange 3 for a regression analysis. I train and cross validate on 80% of data and test the model on the remaining data and save the model. The cross validation scores are ...

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