Predictive models are statistical models whose primary purpose is to predict other observations of a system optimally, as opposed to models whose purpose is to test a particular hypothesis or explain a phenomenon mechanistically. As such, predictive models place less emphasis on interpretability and ...
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5answers
2k views
Alternatives to logistic regression in R
I would like as many algorithms that perform the same task as logistic regression. That is algorithms/models that can give a prediction to a binary response (Y) with some explanatory variable (X).
...
21
votes
12answers
3k views
Practical thoughts on explanatory vs. predictive modeling
This question has been bugging me for some time, and I was going to write a blog post about it. However, I think it is better left for discussion in this forum.
Back in April, I attended a talk at ...
18
votes
7answers
460 views
How can I help ensure testing data does not leak into training data?
Suppose we have someone building a predictive model, but that someone is not necessarily well-versed in proper statistical or machine learning principles. Maybe we are helping that person as they are ...
18
votes
4answers
1k views
Differences between cross validation and bootstrapping to estimate the prediction error
I would like your thoughts about the differences between cross validation and bootstrapping to estimate the prediction error.
Does one work better for small dataset sizes or large datasets?
17
votes
6answers
5k views
Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
Lets assume you are a social science researcher/econometrician trying to find relevant predictors of demand for a service. You have 2 outcome/dependent variables describing the demand (using the ...
17
votes
2answers
273 views
“Interestingness” function for StackExchange questions
I am trying to put together a data-mining package for StackExchange sites and in particular, I am stuck in trying to determine the "most interesting" questions. I would like to use the question score, ...
15
votes
0answers
627 views
Practical thoughts on explanatory vs predictive modeling [duplicate]
Possible Duplicate:
Practical thoughts on explanatory vs. predictive modeling
This question has been bugging me for some time, and I was going to write a blog post about it. However, I ...
13
votes
1answer
1k views
Manually calculated $R^2$ doesn't match up with randomForest() $R^2$ for testing new data
I know this is a fairly specific R question, but I may be thinking about proportion variance explained, $R^2$, incorrectly. Here goes.
I'm trying to use the ...
12
votes
3answers
1k views
Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?
In what circumstances would you want to, or not want to scale or standardize a variable prior to model fitting? And what are the advantages / disadvantages of scaling a variable?
12
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2answers
576 views
Sites for predictive modeling competitions
I participate in predictive modeling competitions on Kaggle, TunedIt, and CrowdAnalytix. I find that these sites are a good way to "work-out" for statistics/machine learning.
Are there any other ...
11
votes
1answer
378 views
Are robust methods really any better?
I have two groups of subjects, A, and B, each with a size of approximately 400, and about 300 predictors. My goal is to build a prediction model for a binary response variable. My customer wants to ...
10
votes
2answers
2k views
When and how to use standardized explanatory variables in linear regression
I have 2 simple questions about linear regression:
When is it advised to standardize the explanatory variables?
Once estimation is carried out with standardized values, how can one predict with new ...
10
votes
1answer
337 views
How to predict when the next event occurs, based on times of previous events?
I'm a high school student and I'm working on a computer programming project, but I don't have a lot of experience in statistics and modeling data beyond a high school statistics course so I'm kinda ...
9
votes
2answers
164 views
Domain-agnostic feature engineering that retains semantic meaning?
Feature engineering is often an important component to machine learning (it was used heavily to win the KDD Cup in 2010). However, I find that most feature engineering techniques either
destroy ...
9
votes
2answers
861 views
Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions
Context:
From a question on Mathematics Stack Exchange (Can I build a program), someone has a set of $x-y$ points, and wants to fit a curve to it, linear, exponential or logarithmic.
The usual ...
9
votes
1answer
259 views
Predictive Modeling - Should we care about mixed modeling?
For predictive modeling, do we need to concern ourselves with statistical concepts such as random effects and non independence of observations (repeated measures)? For example....
I have data from 5 ...
9
votes
1answer
373 views
Predicting long-memory processes
I'm working with a two-state process with $x_t$ in $\{1, -1\}$ for $t = 1, 2, \ldots$
The autocorrelation function is indicative of a process with long-memory, i.e. it displays a power law decay with ...
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 ...
8
votes
3answers
486 views
Statistics for online dating sites
I'm curious how an online dating systems might use survey data to determine matches.
Suppose they have outcome data from past matches (e.g., 1 = happily married, 0 = no 2nd date).
Next, let's ...
8
votes
1answer
875 views
How to do cross-validation with a Cox proportional hazards model?
Suppose I have constructed a prediction model for the occurrence of a particular disease in one dataset (the model building dataset) and now want to check how well the model works in a new dataset ...
7
votes
4answers
759 views
Obtaining a formula for prediction limits in a linear model
Let's take the following example:
set.seed(342)
x1 <- runif(100)
x2 <- runif(100)
y <- x1+x2 + 2*x1*x2 + rnorm(100)
fit <- lm(y~x1*x2)
This creates a ...
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 ...
7
votes
2answers
1k views
Predicting daily electricity load - fitting time series
I want to predict inter-day electricity load. My data are electricity loads for 11 months, sampled in 30 minute intervals. I also got the weather-specific data from a meteorological station ...
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 ...
7
votes
2answers
194 views
General approaches to model car traffic in a parking garage
a friend of mine has asked me to help him with predictive modelling of car traffic in a medium sized parking garage. The garage has its busy and easy days, its peak hours, dead hours opening hours (it ...
7
votes
5answers
236 views
Does preclustering help to build a better predictive model?
For the task of churn modelling I was considering:
Compute k clusters for the data
Build k models for each cluster individually.
The rationale for that is,that there is nothing to prove, that the ...
7
votes
2answers
391 views
Machine learning techniques for time series estimation - forecasting price
Can anyone recommend any machine learning techniques for time series estimation?
I have a series of times $t_{1}...t_{n}$, each having a set of associated features $f_{1}...f_{m}$, and a value $x$.
...
6
votes
5answers
2k views
Recommend some books/articles/guides to enter predictive analytics?
What learning material would you suggest for a CS person / novice statistician / novice mathematician to get into predictive analytics?
6
votes
2answers
952 views
What methods to use for statistical prediction/forecast of trading data?
I’m working on a trading system and need to apply some statistics on the results. Unfortunately I forgot all about statistics after I left university over a decade ago and now I really have no clue ...
6
votes
2answers
250 views
Predicting Y using X for the following data
I have observed the following pattern of behavior in my data-set and I'm wondering how I can explore whether I can predict Y using a value of X. This plot is a 2D histogram of values.
I have ...
6
votes
1answer
429 views
What do Lift and Gain Charts state in the context of an employee turnover model
So I am trying to further understand Lift and Gain charts as it applies to my employee turnover model (i.e. used CHAID in SPSS Modeler). For my data this means predicting number of people voluntarily ...
6
votes
1answer
64 views
How to verify that the model is real?
Here is a block diagram which I'm using when I want to verify that my model is real.
In each round a fold of 11/12 percent of the data is used to bulid the model (e.g. eigenvectors of the PCA)
...
6
votes
2answers
184 views
Neural network model to predict treatment outcome
I want to predict the outcome of a particular treatment (remitted or not) using demographic, plasma biomarker, genetic, and clinical data. IS a neural network model the best way of doing this? What ...
6
votes
2answers
99 views
Is there overfitting in this modellng approach
I recently was told that the process I followed (component of a MS Thesis) could be seen as over-fitting. I am looking to get a better understanding of this and see if others agree.
The objective of ...
6
votes
2answers
162 views
Estimating event probability from historical time series with clear seasonality
I would like to predict the average number of days in a year for which two conditions are true:
daily average temperature is below zero celsius
the day was preceded by at least four days with daily ...
6
votes
2answers
68 views
Weighted discrete measurements of a value changing over time
The best way I can think to describe this question is by example: Imagine there is a ship sailing around the pacific ocean on an unknown path (possibly random.) Other ships passing by sometimes see ...
6
votes
2answers
1k views
Survival Model for Predicting Churn - Time-varying predictors?
All,
I am looking to build a predictive model for predicting churn and looking to use a discrete time survival model fitted to a person-period training dataset (one row for each customer and discrete ...
6
votes
0answers
155 views
Finding the correct data mining approach
(I apologise for being a newb, but I'm a researcher introducing myself to data mining---any help or insight would be greatly appreciated. Also, this isn't technically a homework question, but I've ...
6
votes
1answer
274 views
How can I generate predictions from the randomSurvivalForest package in R?
I'm trying to use the randomSurvivalForest package in R to predict the next event in a series of events (using ...
5
votes
3answers
410 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 ...
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 ...
5
votes
1answer
222 views
Determine accuracy of model which estimates probability of event
I'm modelling an event with two outcomes, a and b. I have created a model which estimates the probability that either a or b will happen (i.e. the model will calculate that a will happen with 40% ...
5
votes
4answers
1k views
Data preparation for regression
I am trying to predict real estate sales prices.
In my dataset there are independent variables that are both nominal and numeric (square meters, prices etc.)
Before feeding the data to any ...
5
votes
2answers
199 views
Visualizing the calibration of predicted probability of a model
Suppose I have a predictive model that produces, for each instance, a probability for each class. Now I recognize that there are many ways to evaluate such a model if I want to use those ...
5
votes
1answer
97 views
What do you do with your testing data?
Suppose you perform a parameter selection study in an environment with not-so-many data points. You divide the available data into training and testing set and you plan to validate the future model on ...
5
votes
2answers
153 views
Time series factor model with one series more frequent
Let's say I have two time series, one of which updates more frequently than the other:
$x_0,x_1,x_2,\dots,x_t,\dots$
$y_0,y_{10},y_{20},\dots,y_{10t},\dots$
I want to fit a model to this that ...
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 ...
5
votes
2answers
584 views
Building background for machine learning for CS student
I am a CS graduate student and I am starting to get really interested in Machine Learning (and Predictive Analytics). I have started working on a text classification project with a professor to learn ...
5
votes
3answers
1k views
Predicting from a simple linear model with lags in R
I have a dataset that I want to fit a simple linear model to, but I want to include the lag of the dependent variable as one of the regressors. Then I want to predict future values of this time series ...
5
votes
1answer
109 views
Predicting a semi-deterministic process
Say I have a process that gives me 3 outputs: $O^1$, $O^2$ and $O^3$.
The outputs are generated from a semi-deterministic process, i.e. there is a deterministic component in the outputs, along with a ...
