Tagged Questions
0
votes
0answers
44 views
When to Log/Exp your Variables when performing Linear Regression?
I'm doing regression using Random Forests for predicting prices based on several attributes. Code is written in Python using Scikit-learn.
How do you decide whether you should transform your ...
0
votes
0answers
11 views
Mean absolute percentage error (MAPE) in Scikit-learn
How can we calculate the Mean absolute percentage error (MAPE) of our predictions using Python and scikit-learn?
From the docs, we have only these 4 metric functions for Regressions:
...
0
votes
0answers
52 views
Statistical tests on the revenue data of a small business
I have daily revenue data from a small business with 6 locations. The business sells food products that range from roughly \$2.00 to \$9.00, mainly to professionals. They do over a million dollars a ...
0
votes
0answers
28 views
Question about predictive bias - intercept and slope bias
I am slightly confused on how to determine a slope and intercept bias. I have an assignment where i am supposed to conduct a gender predictive validity bias analysis.
However, my lab handout and the ...
5
votes
2answers
78 views
Why do categorical predictor variables in regression need to be recoded as multiple predictors?
I'm learning about machine learning using Python's library scikit learn, and in their tutorial here they mentioned about a categorical variable color which can have ...
0
votes
1answer
63 views
Why is Hedonic Regression used instead of Linear Regression
Why is Hedonic Regression used (especially in housing prices) instead of Linear Regression?
There do not seem to be any libraries in Python (and R) for Hedonic regression, is it too niched a ...
0
votes
1answer
46 views
Algorithms for regression analysis which can handle large scale datasets
I am a CS undergraduate student and for my final project i developed a regression algorithm that is suited for large-scale datasets (i wouldn't say 'Big Data', but still large scale).
For the final ...
1
vote
0answers
19 views
Hierachical Predictors in a Regression
Note: Mainly this question pertains to predictions from a model.
If the unit of analysis of a regression (or any predictive model really) is the individual retail store and these stores are organized ...
1
vote
0answers
18 views
Calculating error bars for Excel Linear Regression [duplicate]
I've ben sent a forecast of sales from a consultancy. It uses Excel's LINEST function, taking 4 factors that seem to have affected sales in the past, and used them to make a prediction.
How do I go ...
1
vote
0answers
39 views
Prediction with intervals as the independent variable
I have sample data that maps intervals to a number:
[3,7] => 1
[6,8] => 2
[6,13] => 3
[7,10] => 3
[10,13] => 4
The dependent variable's values ...
2
votes
1answer
139 views
Predicting Football match winners based only on previous data of same match
I'm a huge football(soccer) fan and interested in Machine Learning too. As a project for my ML course I'm trying to build a model that would predict the chance of winning for the home team, given the ...
4
votes
0answers
98 views
Model performance in quantile modelling
I am using quantile regression (for example via gbm or quantreg in R) - not focusing on the median but instead an upper quantile ...
2
votes
1answer
87 views
Predict 2 responses from two co-variates
I'm not quite sure how I should fit a model that has two responses. The data consists of target (x,y) co-ordinates and actual (x,y) co-ordinates. I would like to fit a model to predict a new set of ...
7
votes
5answers
251 views
Classification vs. regression for prediction of the sign of a continuous response variable
Say I want to predict whether or not a project will be profitable. In my sample data, the response variable is actually a continuous variable: the $ profit/loss of the project.
Because my ultimate ...
0
votes
0answers
33 views
Does it make sense to include higher level predictors when there is no higher level variance?
I want to test the relative importance of incident, victim and neighbourhood characteristics on the probability of a crime being reported to the police. I use a three-level random intercept logistic ...
1
vote
2answers
124 views
Predictive model for error of another model
Does it make any sense to build a regression model for a certain target variable on a certain training set. Then build a regression model for the errors of the previous model ( real values vs ...
1
vote
1answer
98 views
Modeling Outliers of Normal Distribtuion
I am using a linear model to predict under-nutrition in children under 5. The common metric discussed is stunting (a binary outcome) which is defined as being more than two standard deviations away ...
3
votes
1answer
150 views
Why would predicted values be normally-distributed when the actual values are uniform?
I'm building a supervised learning model where the target variable is a uniformly-distributed continuous value ranging from 0-1 (originally a rank value from 1-38000, then scaled down to 0-1). The 20 ...
1
vote
2answers
54 views
Predictions when multiple outcomes
Background and Setting
I have data of this format: on each subject the list of exposure to some subtances, some demographics and then a multiple response (whether the subject developed a disease or ...
0
votes
3answers
182 views
Regression model for predicting sales?
We sell machinery. The following is graph is an approximation of units sold over time for a particular piece of equipment1 :
It starts out slow and slowly grows over time. I tried using linear ...
4
votes
1answer
312 views
Verification of a regression model
I need some guidance related to regression model verification using validation data.
I am new to R-tool & statistics and trying my best to learn. I did search on internet too but I couldn't get a ...
1
vote
1answer
82 views
Problems with modeling a cumulative dependant variable
I am building a .NET program. One of its functions is to provide a predictive model for a vehicles life-to-date maintenance costs, basically what is the cumulative cost(Y) for a vehicle at specific ...
0
votes
0answers
224 views
Computing 95% prediction interval of a perfect-fit model
my apologies for my poor knowledge on statistics. Is there a way to calculate the prediction interval at 95% confidence level of a perfect-fit model? I want to compare how my actual model prediction ...
3
votes
0answers
272 views
High-dimensional Regression Datasets [closed]
Am looking for pointers to publicly(online) available high-dimensional regression datasets for evaluating my research work.
By high-dimensional, am looking for regression datsets with the number of ...
0
votes
0answers
113 views
R glmnet - custom measure for cross validation
In R's glmnet package, there are five options available for the type.measure variable in the cv.glmnet class. Is there a way to specify a custom measure for cross validation? Or is it not possible in ...
3
votes
2answers
104 views
Prediction and explanation of user rating based on multiple criteria
I'm trying to figure out a way to both predict how a user would rate a certain document, as well as an explanation of why certain documents are rated a certain way.
A user is represented by:
...
1
vote
1answer
85 views
Robust regularized regression
I've been using elastic net implemented in R (via glmnet) for some modeling, but I was wondering, due to the number of outliers in my data, if there was some sort of modeling approach for regularized ...
2
votes
0answers
57 views
Comparing predictive validity of separate IVs on single DV
I'm attempting to understand some data and would greatly appreciate help in picking the appropriate analysis measure.
Context:
I am conducting psychometric analysis of the predictive validity of ...
1
vote
0answers
46 views
Distance correlation and prediction
If the distance correlation (ref. Gabor J. Szekely) $R_n(X,Y)>R_n(Z,Y)$ would the expected generalization error of a prediction model over $(Z,Y)$ be lower than $(X,Y)$ in predicting $Y$, where ...
7
votes
2answers
212 views
Best way to combine binary and continuous response
I am trying to come up with the best way to predict payment amount for a collections agency. The dependent variable is only non-zero when a payment has been made. Understandably, there are an ...
0
votes
0answers
74 views
Last value in sequence for known trend — is this an app for nonlinear regression?
I would most appreciate names of methods/techniques. Again, I don't have the terminology to describe the problem very well -- I'll edit as needed.
There is a variable (x,y,z) where x is a timeframe ...
0
votes
0answers
14 views
Analysing differences between two variables for the same people at one point in time? [duplicate]
Possible Duplicate:
What is the proper way to analyze discrete data?
I have data from a large scale survey. I am interested in analysing the differences between two variables. Both ...
4
votes
2answers
152 views
Optimizing regression coefficients to predict the largest outcomes
What is a sound methodology to improve the efficiency of the regression coefficients when we are interested in predicting the larger values of the marginal distribution (tails)?
For example, we want ...
1
vote
1answer
548 views
Regression technique for data comprised of categorical explanatory variables & a continuous response variable
i suppose one way to characterize data is by a combination of the variable types that comprises it:
...
3
votes
3answers
513 views
Fitting an exponential mixture model with interval constraints on the mixture weights
What methods are there to fit a model of the form $y=A\mathrm e^{Bx}+C\mathrm e^{Dx}+E$?
Here is the actual scientific data to be fitted: http://dl.dropbox.com/u/39499990/Ben%2C%20real%20data.xlsx
...
1
vote
1answer
115 views
Theoretical problems with modeling auction systems
Lets say that we have an online auction where various known sellers and known buyers exchange product X. A seller will post product X and each seller will then bid in accordance with X and if they ...
1
vote
0answers
133 views
Guassian Process Regression - feature selection
I'm using guassian process regression to do some modeling. One issue I'm encountering is feature selection for some of my models, which often have many relevant features. I'm not sure what the best ...
3
votes
2answers
674 views
Why does Lasso do better than SVM?
This is a soft-question:
I have been evaluation various regression techniques over a regression dataset that I have. I am surprised by the fact that cross-validated RMSE of Lasso is better than SVM ...
5
votes
3answers
324 views
What can I do if my logistic regression model doesn't predict anything?
I have a logistic regression model which predicts win/loss on amount of money paid. I run my model every two hours on new data that I acquire and use it to predict the next two hours. However, I keep ...
1
vote
1answer
555 views
Predict future student outcomes (binary and continuous) with historic cross-sectional data?
Using Stata 11.2, I would like to develop 2 analytic models that could be implemented by school administrators to flag students for intervention. I'm wondering if it would be possible to develop ...
1
vote
0answers
173 views
Evaluating the prediction ability of several alternative models
First off, I apologize if it seems like I'm flooding the board, but I'm new to modelling and a lot of the questions that are coming up aren't closely related to one another, so it seems better to put ...
2
votes
4answers
261 views
Communicating Regression Model Results
I am concerned about how unequipped most people are (both within and without academia) to properly employ standard model building methods such as linear regression and to interpret the results of ...
5
votes
3answers
411 views
Is cross-validation the most important measure of a predictive model's effectiveness?
Why bother with p-values, R squared, etc. ... Model size is not a factor with the computing power available now so why not just run multiple iterations of all possible sets of input variables and see ...
4
votes
4answers
2k views
Why is a zero-intercept linear regression model predicts better than a model with an intercept?
Many textbooks and papers said that intercept should not be suppressed. Recently, I used a training dataset to build a linear regression model with or without an intercept. I was surprised to find ...
2
votes
1answer
157 views
Scoring new observations after cross-validation
I have some doubts about cross validation and scoring a new set of observations.
Let's say I want to predict $y=b_0 + b_1x_1$ and have built a 10-fold cross-validation data set, run a regression ...
1
vote
0answers
146 views
Predictive power (or $R^2$) adjusted for certain variables
I will frame this question for Ordinary Least Square (OLS) regression, but my question is for both OLS and Logistic.
Let's say we data over 10000 different individuals. For each person we have three ...
8
votes
2answers
3k views
Difference between confidence intervals and prediction intervals
For a prediction interval in linear regression you still use $\hat{E}[Y|x] = \hat{\beta_0}+\hat{\beta}_{1}x$ to generate the interval. You also use this to generate a confidence interval of ...
4
votes
2answers
456 views
How to draw a random sample from distribution of prediction?
For my microsimulation, I want to use R to predict values and draw a random sample based on this prediction.
To clarify my point: I want to simulate the number of chronic conditions people suffer ...
1
vote
2answers
183 views
How to predict shares?
Lets say I know what is the overall budget for some units and I want to predict share of budget each unit will get. I have historical data and could do regression analysis. Is it better to predict ...
1
vote
2answers
534 views
How do you apply a linear regression built in SPSS to new data and generate prediction intervals
I am trying to use SPSS to build a linear regression on historical data (dependent and independent variables) and then apply this to new data (independent variables only) to generate predicted values ...
