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Questions tagged [regression]

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

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Finding Impact of IV on NPS of responses in A survey

I have a dataset like this. Where the first column is the age of usage of product of a user (in days) and second column is the response of the user to the typical NPS survey question: Using these ...
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Is employee attrition regression or classification problem?

I'm currently working on employee attrition project. I want to predict the attrition rate of day-n. So, I want to give a report such that probability of an employee to stay in company at day-m, day-m+...
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input transformation for polynomial regression

First I apologize if the question is not very clear as I'm new to this field. I'm doing a university project to create polynomial regression in python without any kind of libraries. In our class the ...
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Keras model structure to get single float result

I need to solve regression problem from 200 float values in range [-1.0 to 1.0] to one float value in the same range. 0.1 and 0.01 here is totally different results. Now I'm trying this model: ...
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1answer
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Regression in Python with many NaN values spread across all columns

I want to do a regression to predict "value" based on the other columns from below example table. The data was collected by single indicator and not across all data points, resulting in many NaN/blank ...
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1answer
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When to build separate models

I'm pretty new to predictive modeling, but am interested in generating predictions for credit card account spend. These are existing accounts. The data I have available to me is Card Type (i.e. ...
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Imaging Scoring using Regression in Orange?

I am using the Orange platform and am trying a use case for scoring cancer cellularity in images. Some facts about the training set: Consists of 2K~ TIF images, for which (2) labels exist: the RID (...
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How to attribute variance to an input parameter?

Some data Maybe this is easiest to explain by going straight with the data. Here is how much money Bob has at the end of each day. ...
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Time Series Prediction for non-uniformly varying dependent variables

I have a dataset with the following properties: DATETIME: range from "01.01.2014 01:00:00" to "12.31.2016 23:00:00" (index) Units: Category (#53) Technology: Category (#5) Capacity: Continuous value ...
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What should be the requirement for training data in order to obtain a good regression model using neural network?

I have made a neural network regression model using the theory for the first time and would like to clarify some basic doubts, whose concrete answers I couldn't find yet. Data:- I have 3000 samples ...
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how to find holiday effect on revenue?

I have 2 datasets from 2013-2017 for each day. a) Revenue generated by Locations and date. b) Holiday name and date I would like to know how each holiday is impacting the revenue by location. I am ...
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What's a good machine learning model for an univariate data set?

Here's my problem scenario:- I have to come up with a power equation as a function of frequency. The plot fits well with a higher order polynomial (4th or 6th) :- $$Power = \theta_0 + \theta_1 fr^1 + ...
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During a regression task, I am getting low R^2 values, but elementwise difference between test set and prediction values is huge

I am doing a random forest regression on my dataset (which has abut 15 input features and 1 target feature). I am getting a decently low R^2 of <1 for both the train and test sets (please do let me ...
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1answer
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Which model can solve the “sequence demand” problem?

I have a regression problem. When a truck comes, it influences the demand of employees for the next 30 days. Additionally the demand depends on the type of truck (when the truck is big, we need more ...
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What is the difference between classification and regression?

I understand classification....a discrete response or category, like animal is dog or cat. The author says..."Regression techniques predict continuous changes such as the change in temperature, power ...
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Which model can solve the “sequence demand”'problem?

I have a regression problem. When a truck comes, it influences the demand of employees for the next 30 days. Additionally the demand depends on the type of truck (when the truck is big, we need more ...
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2answers
28 views

Does the choice of error function impact the model parametrs?

Suppose I have trained a multi variate linear regression model on a particular training set, and the model parameters $\theta=[\theta_1,\theta_2,\ldots, \theta_n]$ were determined by minimizing a cost ...
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Why to use Ridge or Lasso regression?

As I understand, ElasticNet should always perform better or equal to Lasso or Ridge regressions. So I was wondering why do people still use Ridge & Lasso?
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Kernelized Ridge Regression implementation crashing

I am trying to implement kernelized ridge regression. There are 20000 data rows approximately and about 150 features. This is the model being fit: ...
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LSTM regressor seems to clip predictions

I have built a LSTM regressor with an average absolute error of 8%, what I find not bad since it is the first model... However, looking at the predictions, the network seems to be clipping them, I'm ...
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1answer
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Feature Selection Phase

I am trying to predict the overall age of an opportunity (creation date - closing date) this is my response variable lets say an opportunity passes through 3 stages to close For example: Opp x ...
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Training on continuous data for profile regression

I have a large data set consisting of millions of 1-dimensional profiles. The profiles themselves are arbitrarily complicated continuous functions, $f(x)$, each bound from $0 < x < 1$. These ...
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Factor-based performance attribution using rolling period

I would like to decompose the return of a hedge fund portfolio into a set of factors and the residual term, using rolling-period methodology. Let $R_i = B_1*F_1 + B_2*F_2 + B_3*F_3 + e_i$ where $...
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1answer
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P-value mining on large number of combinations of variables

I really don't know any machine learning, but have a problem that seems like one where I should use some ML algorithm. I am analyzing a medical study with one age-related condition, age, a treatment, ...
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Non-linear Support Vector Regression issue - Sklearn Python 3.6

I am fairly new to Sklearn and machine learning and have encountered an issue when using SVR with an RBF kernel. Below is my predicted data compared directly with my real data: I do not know what I ...
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Robust linear regression using RLM

I have regression problem and I am using RLM provided by statmodel in python However, when I fit the data, it gives float division by zero. Any idea why? Here is the code ...
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Multiple formulas for r squared eval metric for regression

I was came across different formulas for R squared on different articles. R Squared = 1 - RSS/TSS R Squared = ESS/TSS RSS -Residual sum of squares. TSS - Total sum of squares. ESS - Explained ...
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Getting NaN output on test predictions, first attempt at building a model

I have been successfully following the tutorial examples on the Tensor Flow site and decided to implement my first model based on the Regression example of the Boston Housing data set. I have split ...
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Time series regression using SVR

I have time series data stored in a data frame as follows: Time, c1, c2, c3 0, 0.55, 0.4 , 0.3 1, 0.8 , 0.1 , 0.6 2, 0.9 , 0.5 , 0.7 .... And I want to ...
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How to weight loss in regression

I've got a regression problem where a model is required to predict a value in the range [0, 1]. I've tried to look at the distribution of the data and and it seems that there are more examples with ...
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2answers
84 views

multivariate clustering, dimensionality reduction and data scalling for regression

I have a dataset with approximately 20000 observations consisting of 40 independent and 1 dependent variable. My initial objective is to develop a model that will predict the dependent variable. I ...
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Regression based on series of depth (similar to time series)

I have a data with set of independent variables and a target variable. The target variable is exponentially distributed based on the depth. Is there a way to identify a general depth function for my ...
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1answer
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Data Augmentation for Regression ANN with low Sample Size

There is a Dataset of 65 tuples. I want to Augment new Data from this set and validate my ANN on the original Data. Is there a possibility, that my ANN already overfits on the augmentet Data. For ...
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1answer
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What are some appropriate models to use for inventory forecast based on consumption history or trends?

I am working on an inventory management system where I have daily/monthly/yearly consumption history for a particular item, which may or may not follow a repeating trend. In order to forecast demands ...
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Classification vs Regression Algorithms - Should exists algorithms only for Classification and/or Regression

Dummy question: There exists algorithms that should only be used for Classification or Regression problems? For example, should Random Forest should only be apply on Classification problems and ...
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Keras TimeSeries - Regression with negative values

I am trying to make regression tasks for time series, my data is like the below, i make window size of 10, and input feature as below, and target is the 5th column. as you see it has data of {70, 110, ...
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1answer
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How to handle missing data data in dependent variable?

I'm solving a ML problem statement where there are around 40k records in the dataset. A dependent variable is given in the question (There are many independent variables). But there are some 2k ...
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Logistic Regression with Non-Integer feature value

Hi I was following the Machine Learning course by Andrew Ng. I found that in regression problems, specially logistic regression they have used integer values for the features which could be plotted in ...
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2answers
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How to model & predict user activity/presence time in a website

I need to make a prediction model based on some historical data from a website's user login system. Suppose my dataset has some features like user login time and logout time for each day for a ...
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Bimodal distribution after log-transformation

I'm trying to predict house prices from a given dataset. Since the distribution of the target variable was skewed: I've done log-transformation: And got a somewhat bimodal distribution. What should ...
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1answer
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How does Implicit Quantile-Regression Network (IQN) differ from QR-DQN?

As a newbie, for several months I browsed the internet hoping to find a user-friendly explanation of the Implicit Quantile Regression Network (IQN). But, it seems there is none at all. How does IQN ...
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1answer
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What the good general regression technqiue for a problem with 50 independent varaibles [closed]

I am a newbie to data science and statistics. I came across this problem, which has 50 independent variables and one dependent variable and trying to identify the good regression technique to start ...
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2answers
75 views

sales price prediction [closed]

I have to find make a classifier for price prediction of a item. The question I have is which columns I should choose for price prediction. Also which machine learning classifier would be good to ...
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1answer
39 views

What could be a dataset in which the presence of an outlier dramatically affects the performance of Ordinary Least Squares (OLS) regression?

I am tasked with giving an example of a dataset in which the presence of an outlier dramatically affects the performance of Ordinary Least Squares (OLS) regression. I've searched and searched the web ...
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Semi-supervised learning for regression

It is mentioned on this page that Label Propagation of scikit-learn can be used for regression also. However, nothing is ...
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Why is my regression predictions values variance different from real values variance

I am modeling a physical process using a regression(XGBoost). I'm looking for ways improving my model, have tried different things without success. Decided to get a better intuition on where my model ...
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1answer
47 views

Can I use categorical and numerical data variable at the same time in randomForest in r?

I have data in which few columns contain categorical data whereas remaining columns contain numerical data. I want to use random forest regressor from the randomForest library in r. So does this ...
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1answer
52 views

How to train f(x)=x*x using Artificial neural network?

let's take some training data of size 100 x_input = [1,2,3,4,.....,100] y_label = [1,4,9,16,....,10000] Now, let's consider that we don't know the function f ...
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1answer
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Why the results from Matlab curve fitting function differ when using custom equation?

I'm using Matlab's curve fitting app, When I select Power fitting it returns values which perfectly describe the data but when I use the custom equation and enter ax^b as the equation it returns very ...
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Linear regression space transformation

Can someone help me how space transformation works on linear regression problems because I have been confused. When we perform space transformation with a function e.g. $\varphi (x)$ we perform the ...