Questions tagged [regression]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
7 views

What is the difference between VAR and VECM model?

What is the difference between VAR and VECM model? And when do I know which one to use?
2
votes
2answers
23 views

Data Imbalance in Regression Tasks [closed]

Having read a lot about class imbalance in classification tasks, I'd like to know what is the methodology for data imbalance in regression tasks. Particularly, - What is the procedure to check for ...
0
votes
2answers
19 views

Custom loss function

Is it possible to apply a custom loss function in a regression model (or any other algorithm for predicting continuous variable) ? I'm working on a stock market prediction model and I need to maximize ...
1
vote
1answer
22 views

Linear regression: variables with high P-value

Often, after training a linear regression model on data, some variables/features would have high P-value, which means they are not statistically significant. Although there are automated methods like ...
0
votes
0answers
11 views

Choosing a non-linear regression model and predicting

I'm new to data science and machine learning. I was working on a project and I happened to get this graph. I want to build a predictive model using this, for each of the boroughs. I do understand ...
0
votes
0answers
16 views

Consecutive Feature Selection-CV and Model Selection-CV

I want to ask a question about general workflow of algorithm development. I want to include a "feature selection with Random Forest" step into my workflow but I have doubts about data leakage. It is ...
0
votes
2answers
13 views

Regression error increase after shuffing data

I'm trying to do multivariate regression using a 3-dimension data set. I noticed a strange problem that my fitting error increase dramatically after I pre-shuffled the data matrix comparing using ...
0
votes
1answer
17 views

Machine Learning algorithm for predicting number of cases in pandemic

I’m giving my first steps with AI and Machine Learning so I have the following issue. I’m trying to predict an outcome from COVID-19 number of day vs confirmed cases using scikit-learn library. I mean,...
0
votes
0answers
6 views

Gamma objective function XGBoost

I am using XGBoost to predict a variable that is highly skewed and always is greater than zero. I did a significant search to see some materials for gamma objective function in XGBoost but I could not ...
0
votes
0answers
16 views

Splitting a 10 year long time-series into multiple year time-series on Deep Learning Models

I'm using recent Deep Learning models for time series analysis such as DeepAR[1] and DeepFactors[2] for my masters. My target time series was given to me by a cement factory, 10 years of compositions ...
0
votes
0answers
12 views

Visualize Regression Parameters with Reference Level in R or Python

I am having a regression with quite a lot of Categorical features. How can I display the coefficients (and the standard deviations of those) including the reference level for every category. Thus far ...
1
vote
0answers
11 views

Selecting Regressors from list for time series

I have been try to get results from a time series problem and I have used fb's prophet algo for that. Now problem is I have to select comb of those regressors giving me least rmse. I used granger ...
0
votes
0answers
20 views

Normalizing dependent feature by one of the independent ones

I have a data set with three different features (x1, x2, x3) and I am going to use a regression model to predict y based on the features. x3 is the total amount of money that a customer invest and y ...
0
votes
0answers
17 views

Transformer-based architectures for regression tasks

As far as I've seen, transformer-based architectures are always trained with classification tasks (one-hot text tokens for example). Are you aware of any architectures using attention and solving ...
0
votes
0answers
9 views

How to compute score on this pre-defined accuracy metric? [closed]

I have a dataset where I have to predict the predicted_price, which has a time-series data. I have successfully done a feature engineering on the dataset to extract the DateTime features. Following is ...
1
vote
0answers
10 views

Can i use other regression types that arent based in decision trees to use it like a weak learners in gradient boosting?

I was thinking if i can use polynomial regression like a weak learners in gradient boosting but i read that decision trees are used for that and i cannot find anything that show me the possibility of ...
0
votes
0answers
22 views

How to label data to build a model from scratch

I have different texts, some of them have been manually labelled some others have not. Texts “The modelling dataset were rebuilt yesterday” “This movie is awful” “Trump said that ufo exist” I would ...
-1
votes
0answers
6 views

default product ranking system for ecommerce website: predict ctr [closed]

I am developing a default ranking system for our website. The products will be ranked based on predicted CTR. There are a few variables which will help to predict ctr. I transformed the data using ...
0
votes
0answers
26 views

High dimensional data and missing values [closed]

I am working on a high dimensional data set with a very large number of missing values in almost all of the columns. Some columns may have several nans, some of them can have nans of ~50% of the whole ...
0
votes
2answers
11 views

Regression and Machine learning models for panel data

I have a panel data which consists of the sales of different companies across different years. Can someone tell me what kinds of regressiona and ML methods we use for forecasting the sales? Thanks
0
votes
0answers
16 views

Defining the Target Value

im new to this community and it always helped me with my concerns, i looked for an answer but didnt find a clear one yet im working on study for insurance default, the data i received is already ...
0
votes
0answers
8 views

News Recommendation Engine with XGBoost

I want to build a news recommendation engine with XGBoost, but the data I have contains implicit user ratings, view history of a user. I know what my X's will(user embeddings + Item Embeddings) be but ...
-1
votes
0answers
44 views

TypeError: unhashable type: 'numpy.ndarray' (Simple Linear Regression)

Below is my code, when I am executing this code I am getting the TypeError: unhashable type: 'numpy.ndarray'error. Can anyone help me? ...
0
votes
0answers
9 views

Which Coefficients Should Be Used For Imputation of Validation Data

Suppose missing values in the training set imputed by using a regression model. In testing phase, should coefficients from regression model which used for imputation of training set be used to impute ...
0
votes
0answers
15 views

How do I create these Kernel functions in Python for Gaussian Process Regression?

I have a dataset of 1031 observed samples of 7 features that form the X and one target variable that forms the Y. I am using Gaussian Process Regressor to train my models. I want to use anisotropic ...
-1
votes
0answers
11 views

Polynomial Regression for Matrix

I am new to Python. I have a problem and I hope you can help me with this problem. I have 2 numpy array matrices: $X$ and $Y$. Each matrix has 652 rows and 652 columns. $X^T = X$, $Y^T = Y$. I must ...
0
votes
0answers
20 views

Keras stacked model difficulties

I am solving regression problem now and unfortunately I'm stuck. I have a dataset containing train, test dataframes (~5000 rows each) and images with common ID with dataframes records. First ...
1
vote
1answer
15 views

Memory allocating error with coxph model

I'm working with cox models and as I am adding more variables, I am facing a memory issue. I tried to subset my dataframe by selecting columns which have variables of interest but even then I am ...
1
vote
2answers
22 views

Training, cross validation and testing accuracy (RMSE and R2) differs when using different shuffles and splits

I have a very small data set of 60 observations. My training, cross-validation and testing accuracy (RMSE and R-squared) differ in a considerable amount when using different random states while ...
4
votes
1answer
23 views

Spatially constrained geospatial similarity

What's the current methodology for clustering geospatial data by features? Example: I have some demographic dataset. Let's say this contains average home price and population density. So, an example ...
0
votes
0answers
8 views

Preprocessing targets for multi- output regression

I am trying to predict two continuous outputs, both in the range of [-0.52 0.52] using a neural network. But however hard I am trying, I am not getting satisfactory results. The network has two input ...
0
votes
0answers
9 views

Determining the degree of freedom in ridge regression | Interpretation of the head matrix

right now, I am diving into statistical learning and stumbled over the so-called "head-matrix" and the determination of the degree of freedom. I am referring to ridge regression: So the ...
1
vote
1answer
47 views

How can I improve my model on a very very small dataset?

I am starting as a PhD student and we want to find appropriate materials (with certain qualities) from basic chemical properties like charge, etc. There are a lot of models and datasets in similar ...
0
votes
0answers
18 views

Multi output regression analysis learning curve

I am quite new in data science. I have been following Andrew Ng's machine learning course. I am employing neural networks for a 2-input 2-output regression problem for a robotics project The ...
-2
votes
1answer
24 views

Limitations of Regression in ML?

I've been learning some of the core concepts of ML lately and writing code using the Sklearn library. After some basic practice, I tried my hand at the AirBnb NYC dataset from kaggle (which has around ...
1
vote
1answer
26 views

What's the best way to predict weekly selling data?

I am trying to create a model to predict the units that will be sold for different grocery items say in the next week. I am structuring the problem in a three-step procedure. Group together the ...
0
votes
0answers
20 views

how do I approach forecasting problems using deep neural networks?

I am new to machine learning in general, and I have been requested to predict a price given a date. I have been trying to make a neural network for the task but it does poorly in the testing set, so I ...
0
votes
1answer
37 views

Two-class model with predicted scores needed - classification or regression approach

In my problem, step one is to build a model to classify cases as one of True or False (1 or 0 could also be used obviously). Once the optimum model is found, step two is to retrieve probabilities for ...
5
votes
3answers
105 views

Problem with basic understanding of polynomial regression

I have an understanding of simple linear regression. Clear that results in a fitted line like this: However, studying polynomial regression is a bit of a challenge having some questions about the ...
0
votes
0answers
15 views

Weighting for spatial raster as Training Data

I have a spatial raster which I am using as Input for a Random Forest Regression Model. My Goal is a prediction of occurrences of a certain property of individuals for each cell based on cell ...
1
vote
1answer
25 views

How do I predict new/unknown data in Bayesian linear regression?

This is my first question on this forum. I just got started with Bayesian statistics. While I do understand the motivations behind Bayesian methods, I am a little unclear on what the predictions even ...
0
votes
1answer
8 views

Preparing multiple training time-series for Keras LSTM regression model training

I have training data organised in a numpy array in which: * column is feature - last one is the target, * every row is one observation. The thing is that this 2D ...
0
votes
0answers
12 views

How can I estimate the equation (frequency, period, etc) of data which have periodical behaviour?

I would like to estimate the parameters of this equation: $Y = A sin(B x + C) + D $ Which method is the best suitable for calculating this?
2
votes
1answer
28 views

Regularization for intercept parameter

Why is the regularization parameter not applied to the intercept parameter? From what I have read about the cost functions for Linear and Logistic regression, the regularization parameter (λ) is ...
0
votes
0answers
12 views

Applying clustering to predicted values

I am using clustering techniques such as hierarchical clustering trees to create an index fund modeled on the S&P500 with the correlation between the returns of individual stocks being used as the ...
6
votes
2answers
131 views

Confidence interval interpretation in linear regression when errors are not normally distributed

I've read that "If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow" (source). So, can anyone elaborate on this? When are the confidence intervals ...
0
votes
1answer
38 views

Time series regression forecast next month from now (random forest,Lasso,Ridge)

I have a dataset about hedge funds. Its include data from 2010 january to 2019 december. This data are monthly financial ratios of hedge funds such as sharpe,alpha,beta,sortino and monthly returns of ...
0
votes
0answers
18 views

Classification of OLS regression coefficients

A variable $A$ (reaction time) is log-normally distributed, i.e. $\log(A) \sim \mathcal{N}(0,\sigma^2)$ and is linearly dependent of $n$ variables $X = (X_1,\ldots,X_n)$, i.e. \begin{align} A &= \...
1
vote
2answers
21 views

How to choose best model for Regression?

I'm building a model to predict the flight delay. My dataset contains the following columns: FL_DATE (contains months(1-12)), OP_CARRIER (One hot encoded data of Carrier names), ORIGIN(One hot ...
1
vote
2answers
44 views

How can I fix regression model interpretation of feature?

I'm building a regression model to predict the values of a feature $Y$ given a set of other features $X_{1}, X_{2}, X_{3}..X_{n}$. Onde of these other features, let's say $X_1$, is known to be ...

1
2 3 4 5
20