Questions tagged [regression]

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

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REGRESSION PROBLEM_ Generating y from X and error

Good Morning. I am working on an assignment where i need to perform the following tasks: 1.Create 500 observations based on standard normal distribution and store them in variable X 2.Create 500 ...
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Date time conversion in a CSV column

I am new to data science. I am attempting to write a program using regression techniques, and all of my values are numerical, except for the date and time (UTC), which are written in this format: HH:...
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change target variable value to reflect better affordability

Context I am working on a regression problem trying to predict affordability. My dataset contains daily installments repaying a purchase in a form of contract. Essentially, a minimum daily rate the ...
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15 views

Understanding Residuals Plots

I have a residuals plot: Definitions: let's call "blue_line" the line that would exist if I were to draw a straight line by fitting to the blue dots (predictions). My expectation is that if ...
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1answer
27 views

Points to remember when embarking on an organization-wide turn to AI solutions

In our organization, we are currently in the phase of building up team, skills to automate and implement AI based solutions. So, we are very early in this AI journey. Right now, we are also working on ...
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(R) can I convert a categorical variable into a numeric equivalent in linear regression to predict a continuous variable?

Specifically, I have an item code as one of the independent variables that can have several hundred possible values results in underfitting when predicting the projected availability of that item. I'd ...
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Least mean square linear regression with discrete values on y-axis

So, the idea is that I have floating point numbers on x-axis and discrete values (colors) on y-axis. X-axis stands for measured values (temperature) and y-axis stands for predicted values (colors), ...
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Find $a, b, c$ minimizing MSE

Suppose you are given a "dummy" classifier. It looks like this: $$ y(x) = \begin{cases} a \text{ if } x >= c \\ b \text{ else } \end{cases} $$ Given some data set $\{(y_1, x_1), \dots (...
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regression quality with meta score using R2 and MAE for optimisation

Considering quality of regression models I currently try to compare two types of information: The $R^2$ score that give me the information about the tendency of the predictor The $MAE$ (or $RMSE$) ...
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34 views

How to find interval for a series of values? [closed]

I have an example dataset ; 50 60 90 120 140 160 **200** So I want to compute the interval for this series last element. (200) I want to say that 200 is ok or not ...
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product of coefficients of regressions x to y and y to x

I was asked that if $y\sim N(0,a^2)$ and $x\sim N(0,b^2),$ what is the product of coefficients of the regression x to y and the regression y to x. I think this is a very popular question for OLS (...
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compare SVR with medium regression

We usually compare Supporting vector regression (SVR): $$\mathcal{L} = C\sum\limits_{n=1}^{N}\Big(|y_i - g(x_i)| - \epsilon\Big)^+ + \dfrac{1}{2}||w||^2.$$ and ridge regression (RR): $$\mathcal{L} = \...
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Network to find value of specific point in image

We have an greyscale image. Each pixel represents the intensity in range 0 - 255. This intensity can be linearly remapped to value M in different range (e.g. 20 - 50). The image contains some object ...
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Feature Importance interpretation

I want to audit the results of regressions I ran, and hopefully gain more insights about a treatment effect through sklearn's feature importance function (...
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Sktime import tsregression.org (.ts) file giving format error

I have to import these dataset (e.g AppliancesEnergy) from http://tseregression.org/, in order to do some Regression using XGBoost algorithm, these are .ts files. I've followed the tutorial in https://...
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Which regression models should be used with very tiny dataset?

I have a very tiny dataset to make a regression model. only 22 data points with just 2 float features and 1 float output. I want to make models among sklearn ...
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146 views

Influence of imbalanced feature on prediction

I want to use XGB regression. the dataframe is coneptually similar to this table: ...
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47 views

Why is cross entropy based on Bernoulli or Multinoulli probability distribution?

When we use logistic regression, we use cross entropy as the loss function. However, based on my understanding and https://machinelearningmastery.com/cross-entropy-for-machine-learning/, cross entropy ...
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Support Vector Regression for Time-Series Model

As the title is clear, I would like to know it is possible to use SVR (Support Vector Regression) algorithm for Time-series problems?
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Training neural network for regression with gaussian output layer

How does one train a neural network model that does regression over real values, using a gaussian output layer? ie estimating the mean and std parameters of the prediction. Since during training there ...
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Confidence/Prediction Interval in Recursive Least Square(RLS)0

I am trying to implement RLS based on the given algorithm: 1https://en.wikipedia.org/wiki/Recursive_least_squares_filter [] The missing piece is how to update residual mean and variance step by step ...
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What is the best statistical test to compare the competitiveness of 10 football/soccer leagues within a time range?

I was wondering what stat test is the best to find out which football/soccer league is the most competitive. The leagues that I would choose for this analysis are either based on the countries that ...
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Predict recovered amount of credit?

I would like to understand which is the best Machine Learning approach (regression, classification, ...) in the following scenario: I have a dataset with hundreds of people, each of them with a credit ...
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Regression followed by thresholding to predict rare events

I have a multi-variate time series for which I am performing forecasting by regression. My aim is to forecast extreme values in this time series (rare events). On the one hand I have a regression ...
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29 views

Residual plot understanding

I am trying to build a regression model to predict Gerrit code review delay (i.e the time between the creation time of the code review until the time of the last update.) For that, I used a random ...
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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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49 views

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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46 views

What linear model is common practice to use?

I'm trying to develop a regression model. A possibility is to derive more features of course. The final goal is to find the model with the best results on predicting test set. Are there any guidelines/...
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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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34 views

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
22 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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1answer
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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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70 views

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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1answer
32 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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26 views

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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1answer
25 views

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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18 views

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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1answer
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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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5 views

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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