Questions tagged [regression]

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

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198 views

Regression: Scatterplot with low R squared and high p-values

Based on three datasets, I have produced the scatterplot below in Python: I am trying to fit a line on each dataset, but when I check the metrics this is what I get: Set 1 (red): R squared=0.002,...
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2answers
470 views

Which Spark MLlib regression algorithm is suitable for numeric predictions based on non-numeric features?

I am working on Spark MLlib and have a project where I have to make predictions for numeric data based on non-numeric features. I am a bit confused about which <...
13
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2answers
6k views

What to do when testing data has less features than training data?

Let's say we are predicting the sales of a shop and my training data has two sets of features: One about the store sales with the dates (the field "Store" is not unique) One about the store types (...
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4answers
382 views

Non-linear regression line fit

I performed a regression analysis with two datasets, each of which has size 50. One dataset is called Spatial % and the other <...
3
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2answers
476 views

How to model compositional data?

What is the best way to model compositional data problems? Compositional data is when each example or sample is a vector that sums to 1 (or 100%). In my case, I am interested in the composition of ...
18
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2answers
33k views

Multivariate linear regression in Python

I'm looking for a Python package that implements multivariate linear regression. (Terminological note: multivariate regression deals with the case where there are more than one dependent variables ...
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1answer
940 views

regression to model exponential distribution

I have a work order system. I found out that the work order completion times are exponentially distributed. Every ticket has some features. I understand that regression is "a line that best fits the ...
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0answers
166 views

Using previous hour's value in time series data for inclusion in random forest

I have a training data set, which is something like the following: ...
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1answer
674 views

How to choose the order in which to split a decision tree?

I know that a decision tree recursively splits along each attribute, greedily minimizing the wrong classifications/deviance at each split. But, what is the order in which the attributes are split? ...
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3answers
5k views

Is there a library that would perform segmented linear regression in python?

There is a package named segmented in R. Is there a similar package in python?
5
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1answer
159 views

Predictive models with class value belonging to a set of observations

I would like to know whether it's possible to build a predictive model where I could define a set of rows with their attributes, and a class belonging to that set of rows, instead of having the ...
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1answer
6k views

SKNN regression problem

I am trying to learn scikit-learn neuralnetwork and am coming up against the same problem in regression where no matter the ...
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2answers
5k views

How to make predictions based on correlations?

I have correlation values for profit based on three different attributes - attribute1,...
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3answers
10k views

How will ADA Boost be used for solving regression problems?

I have an idea of how ADABOOST will be used for classification but I want to get the idea of how to re-weight and thus use ADABOOST in case of regression problems.
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2answers
120 views

What does the Ip mean in the Bayesian Ridge Regression formula?

From http://scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression, they gave the bayesian ridge distribution as this: $p(w|\lambda) = \mathcal{N}(w|0,\lambda^{-1}{I_{p}})$ And ...
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0answers
205 views

Find correlation in observed data

I have a method that calculates a certain variable. This variable is biased by an error which should be related to the observables that were used to calculated the variable. I don't have an exact ...
4
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2answers
1k views

Interpreting the evaluation result of multiple linear regression

I am learning the multiple linear regression model. I've built a model and using R command: summary(model) I got this result: ...
7
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2answers
1k views

Theoretical bound - regression error

The Bayes error rate is a theoretical bound that determines the lowest possible error rate for a classification problem, given some data. I was wondering whether an equivalent concept exists for the ...
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1answer
100 views

Self-designed objective for linear regression learning

A multiple linear regression is to use several predictor variables to predict the outcome of a response variable, like the following relationship: $y_{i}=\beta_{1}x_{i1}+...+\beta_{p}x_{ip}+\epsilon_{...
3
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1answer
2k views

Implementing sklearn.linear_model.SGDClassifier using python

I have an excel file that contains details related to determining the quality of a wine and I want to implement the linear model concept using the function sklearn.linear_model.SGDClassifier(SVM => ...
3
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1answer
57 views

How to fit an odd relationship with a function?

Let's say there is a function $f$ such that $y = f(x)$. However, if $f$ is a piecewise function such that: $$y = \begin{cases} 0 \quad x \leq 0 \\ 1 \quad x >0\end{cases} $$ How do I fit $f$ in ...
5
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1answer
540 views

Comparing accuracy of models in ordinal regression / classification

I am looking into creating a model to predict whether an item is "Very Good", "Good", "Bad" or "Very Bad". After I fit the training data to the models, comparing the accuracy of the models during ...
6
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1answer
907 views

Choosing best methods for estimating the unknown parameters in a linear regression model

Given some dataset for prediction, for eg say I have different housing price prediction dataset: dataset 1 : 100 training and 100 testing sample, 50 feature dataset 2 : 100 training and 100 ...
4
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1answer
297 views

Minimize correlation between input and output of black box system

I am not sure if "minimize correlation" is the right title for this issue but I could not find a better sentence to describe what I would like to achieve. Let's say that I have a black box with ...
12
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2answers
1k views

Airline Fares - What analysis should be used to detect competitive price-setting behavior and price correlations?

I want to investigate price-setting behavior of airlines -- specifically how airlines react to competitors pricing. As I would say my knowledge about more complex analysis is quite limited I've done ...
1
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1answer
773 views

Basic ML based Prediction model

I had this basic query on ML and would like to get basic ideas on modelling prediction models using ML and Python. Say I have a training data of 1000 items as Item_name, Attrib_1, Attrib_2, Attrib_3,...
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1answer
2k views

Cost function for support vector regression

The optimisation problem for support vector regression is (see http://alex.smola.org/papers/2003/SmoSch03b.pdf): minimise: \begin{align*} C\sum_{i=0}^{l}(\xi_{i} +\xi^{*}_{i})+ \frac{1}{2}\lVert w \...
3
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1answer
520 views

How to visualize multivariate regression results

Are there commonly accepted ways to visualize the results of a multivariate regression for a non-quantitative audience? In particular, I'm asking how one should present data on coefficients and T ...
1
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2answers
803 views

training neural net with multiple sets of time-series data

I have the following data ($x^1_i$, $y^1_i$) for $i=1,2,...N_1$ ($x^2_i$, $y^2_i$) for $i=1,2,...N_2$ ... ($x^m_i$, $y^m_i$) for $i=1,2,...N_m$ Is it possible to train a neural net to produce ...
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4answers
206 views

Format of CSV file

I'am trying to create a regression based prediction (like booking website): predict the number of clicks for each hotel. I have to generate a .csv file containing two columns: ...
9
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2answers
7k views

Proper way of fighting negative outputs of a regression algorithms where output must be positive all the way

Maybe it is a bit general question. I am trying to solve various regression tasks and I try various algorithms for them. For example, multivariate linear regression or an SVR. I know that the output ...
5
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3answers
899 views

Quasi-categorical variables - any ideas?

Let's say I'm trying to predict a person's electricity consumption, using the time of day as a predictor (hours 00-23), and further assume I have a hefty but finite amount of historical measurements. ...
1
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1answer
190 views

Does high error rate in regression imply the data set is unpredictable?

I have a data set of video watching records in a 3G network. In this data set, 2 different kind of features are included: user-side information, e.g., age, gender, data plan and etc; Video watching ...
0
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1answer
159 views

How to model this "un predicatability" problem?

Imagine modeling the "input(plaintext) - output(ciphertext)" pairs of an encryption algorithm as a data science problem. Very informally, the strength of an encryption scheme is measured by the ...
2
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1answer
148 views

Linear combination of weak estimators over fuzzy classifiers?

Having: a set of soft fuzzy classifiers (classification onto overlapping sets) $C_i(x) \to [0,1]$; a corresponding set of weak estimators $R_i(z)$ of the form $R_i(z) = \mathit{EX}(y\mid z)$. The ...
2
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0answers
480 views

How to Interpret Multinomial Specification in R's `mnlogit` package

The mnlogit package in R allows for the fast estimation of multinomial logit models. The specification of forumlas is a bit different from most other regression ...
11
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3answers
1k views

What regression to use to calculate the result of election in a multiparty system?

I want to make a prediction for the result of the parliamentary elections. My output will be the % each party receives. There is more than 2 parties so logistic regression is not a viable option. I ...
3
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1answer
207 views

Modeling when the response variable has too many 0's and few continuous values?

For problems where the data represents online fraud or insurance (where each row represents a transaction), it is typical for the response variable to denote the value of fraud committed in dollars. ...
2
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0answers
122 views

FUZZY ARTMAP for continuous data [closed]

I was going through an IEEE research paper which has used Fuzzy ARTMAP for predicting the price of electricity given some highly correlated data. As per my basic understanding about Fuzzy ARTMAP, it ...
15
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3answers
5k views

Modelling Unevenly Spaced Time Series

I have a continuous variable, sampled over a period of a year at irregular intervals. Some days have more than one observation per hour, while other periods have nothing for days. This makes it ...
2
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2answers
326 views

Regression Model for explained model(Details inside)

I am kind of a newbie on machine learning and I would like to ask some questions based on a problem I have . Let's say I have x y z as variable and I have values of these variables as time progresses ...
2
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1answer
68 views

Adaptive regression parameter estimation in R

How would I do parameter estimation and prediction for the adaptive regression model using R, as in the 4th page of the paper linked below? http://papers.ssrn.com/sol3/papers.cfm?abstract_id=923635 ...
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2answers
1k views

What is the best Data Mining algorithm for prediction based on a single variable?

I have a variable whose value I would like to predict, and I would like to use only one variable as predictor. For instance, predict traffic density based on weather. Initially, I thought about using ...
10
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2answers
9k views

Stochastic gradient descent based on vector operations?

let's assume that I want to train a stochastic gradient descent regression algorithm using a dataset that has N samples. Since the size of the dataset is fixed, I will reuse the data T times. At each ...
6
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3answers
162 views

Can we quantify how position within search results is related to click-through probability?

Suppose, for example, that the first search result on a page of Google search results is swapped with the second result. How much would this change the click-through probabilities of the two results? ...
4
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2answers
1k views

Type of regression with nominal, ordinal, interval and ratio data

Statement of problem: An ambulance is at the hospital dropping off a patient. The goal of the paramedic is to get released from the hospital as soon as possible. I am curious, what are the factors in ...
3
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2answers
167 views

Extrapolating GLM coefficients for year a product was sold into future years?

I've fit a GLM (Poisson) to a data set where one of the variables is categorical for the year a customer bought a product from my company, ranging from 1999 to 2012. There's a linear trend of the ...
11
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2answers
3k views

Solving a system of equations with sparse data

I am attempting to solve a set of equations which has 40 independent variables (x1, ..., x40) and one dependent variable (y). The total number of equations (number of rows) is ~300, and I want to ...

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