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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Which model to predict air cleanness (air pollution) in daily-basis?

How hard it is to predicting the cleanness of air? My friend is an agronomist, he is doing some research on some small plants. The plants are very sensitive to air pollution in urban area (need deep ...
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13 views

cumulative uncertainty with time series predictive model

So I have a time-series with a set of variables a, b, c... and another measured variable y. What I do is using the initial state of a,b,c and y (at t0), I predict what y "should" be at the next time ...
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2answers
30 views

Class Imbalance

What are the best practices for fitting a binomial classification model when the classes are very imbalanced? For example, 99.9% 1's and 0.1% 0's.
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2answers
34 views

“Select All That Apply” — How To Generate a Predictive Model with this Type of Question

For a particular question in a survey, respondents have been asked to select all that apply (i.e. say from a list of books they have read). I'm wondering if anyone knows how I would be able to build ...
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1answer
32 views

Finding predictors of upper level of a variable

I am analyzing data on a health variable and its relation to age, gender, height etc. I am more interested in 90th percentile of the health variable, which can be called upper limit of 'normal'. How ...
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1answer
60 views

Multiple regression or anova or bestglm or forestplot or Boruta

I have data on a continuous health variable and following others: age, gender, height, weight, waist, city and season. I applied multiple regression and got following output: (age, gender, height, ...
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18 views

Decision Tree Modelling

Can anyone explain me about Decision tree parameters - minSplit, minBucket, Complexity, minDepth with some simple decision tree example? And how this parameters will affect the accuracy measure? ...
2
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2answers
69 views

Modelling Technique

I have a 5 years of Cargo insurance (goods transportation insurance) data. I need to predict the claim amount based on their policy date and some other variables like mode of transportation, Country ...
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8 views

What is the test error, if response in test set is missing?

I am given two data set: one used for train model, and one for prediction. However, there is no response variable in the second data. I was asked to test the model built from the first data on second ...
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1answer
19 views

Which performance measure to report?

I've trained a random forest regression model using boot632 resampling and the caret package. The output of the model tuning process gives a few different performance measures. ...
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1answer
13 views

Incremental improvement for boosting

By adding additional factors, will the fitting result of a boosting algo (say Ada boosting) guaranteed to be improved? From my experiment, adding additional factors could make the prediction accuracy ...
0
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1answer
69 views

How to handle zeros in target variable

I'm working on a college assignment in introductory statistics to try to predict a certain target variable. The variable is continuous but has a high percentage (60%) with zero values. This is not bad ...
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1answer
13 views

Prediction on Interaction Terms in Multiple Linear Model

I have created a MLR model where my predictor variables are continuous and categorical. I am interested in the interactions between the categorical variables. Let's say I have the response variable ...
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1answer
27 views

Method for solving problem with variable number of predictors (repost from Data Science)

REPOST from Data Science: I've been toying with this idea for a while. I think there is probably some method in the text mining literature, but I haven't come across anything just right... What ...
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18 views

How to add one covariate at a time, for neural network, lm, or tree models

I have about 26 dependent variables and 400 to 1200 independent variables with 18000 observations. Is there an R package for adding one variable at a time to identify the variable(s) that reduce the ...
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0answers
19 views

SVM predictions of timeseries (forex) data are shifted

I am trying to build timeseries prediction SVM (regression variety) for forex data based on lagged close data. And I am using R. Please see the simple code below and resulting graph, using e1071 ...
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2answers
49 views

How to compare predictions from MLE-based regression Vs. predictions from bayesian regression?

Say I have two linear regression models that I want to use for predictions. Linear regression: \begin{equation} \mathbf{y} \sim \mathcal{N}(\mathbf{X^Tb}, \Sigma_y) \end{equation} Bayesian linear ...
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52 views

fitting a model for time series data

Folks, I am working on time series traffic data where the waiting times are indexed over time, with 288 observations for 24 hour time period (interval of 5 minutes). I am trying to cleanse the data, ...
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1answer
109 views

I would like to analyze the data using R and Python. How to handle 10 millions records of data? [closed]

I would like to analyze the data using R and Python, and I am unable to open text file due to huge number of records. Is there any free database software to install in PC and load the data into it and ...
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1answer
69 views

Increasing sample size with bootstrap sampling

I'm trying to perform some classification analyses with a relatively small dataset (201 observations, 32 predictors). There are 8 classes in my data with unequal sample sizes ranging from 10 in the ...
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0answers
22 views

difference between confidence interval and prediction interval in the context of regression analysis and predictive modeling

When building prediction models, I always see the following concept 1) Confidence interval for regression model 2) Prediction interval 3) Confidence interval for predicted value I can understand ...
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0answers
33 views

Machine Learning Predictors Evaluation Using R

I've bee using R for predicitve analytics and here is issue: I'm trying to predict the species (categorical variables E1, E2, E3 and E4) of an animal using as predictors a set of nominal (NO1, NO2, ...
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0answers
13 views

Autoregressive model - predictive power

I have estimated a VAR (vector autoregressive) model on credit growth in STATA. I want to test its predictive power by comparing its estimated credit growth to observed credit growth (correlation ...
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1answer
39 views

Machine learning algorithm to predict next user's destination

I'm searching for a way to formulate my problem as a machine learning problem. Suppose I have a history of user's locations, and I want to predict his next location, similar to how Google Now does it ...
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0answers
50 views

Linear regression with log dependent variable

I have the following regression: $log(Y) = \alpha + \beta X + \epsilon$ with $E[\epsilon] = 0$ and $var(\epsilon) = \sigma^2$. There is no assumption on the distribution of the errors $\epsilon$. In ...
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0answers
39 views

Trying to find a classifier that will give me probability predictions between 0-1 in weka

This is the first time I've done any sort of predictive modelling and I think I've really confused myself. I have a training set of data with a column at the end that has either a 1 or a 0 in it. ...
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1answer
35 views

Are insignificant variables included in calculation of predicted probabilities?

When calculating the predicted probabilities in a logistic regression model, do we consider all the variables or just the significant ones? For eg: Let's say my model has: dependent variable Y and 3 ...
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19 views

Predicting the near-future values using an unevenly sampled time-series data

Summary Need help with predicting the near-future values using an unevenly sampled time-series data. Data is collected as events, and is converted to time series. I have tried out a few approached ...
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6 views

how do i differentiate between alternative specific and alternative invariants using sas?

Okay so my level of expertise in statistics is fresher/rookie. I am trying to predict the the outcome of categorical choices for a product category y (3 choices or 3 flavors of cereal for example). ...
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0answers
18 views

input variables with different order of magnitude [duplicate]

I need to build a prediction model based on a data set with 5 different independent variables. The data set looks like as follows. The variables in col4 and ...
1
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1answer
61 views

Ratio between positive and negative examples in a training problem

When training a 0/1 classifier, what should be the ratio of positive to negative, how to decide the ratio between them based on the classifier I use and the data set under analysis?
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1answer
69 views

Weighted least-squares negative fitted values

I am running a weighted least-squares regression (where all weights are strictly positive), where my dependent variable is a cross-section of variance values. Since variance is always positive (>=0), ...
0
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0answers
56 views

variable error on logistic regression/ proc catmod- Building predictive model

I am using logistic regression to fit a model with categorical/multinomial varaibles. data-description: There are over 300 variables as independent variables, sample size is 5000 which is divided into ...
0
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1answer
40 views

How to compare probabilistic classifiers?

Assume that we have a very long sequence (i.e. a list) of nominal-valued observations. For example: A A C B ... B A B C We have also a corresponding sequence of ...
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35 views

Are LASSO regression predicted values also biased?

Since LASSO regression biases coefficients to reduce variance, aren't the predicted values also biased? In my case I am looking at fitted values from a predictive logistic regression model with LASSO ...
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17 views

Kolmogorov Smirnoff and Predictive Analytics

I am trying to interpret the Kolmogorof Smirnoff test in the case of predicitve analytics to compare three models: a neural network, a decision tree and a logistic regression. The target variable is ...
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17 views

How do I best possible model future risks for Organization?

Using following concept is it possible to define or layout future risk or in security terms future root-causes that are critical for organization operations and businesses. Those concepts are:- 4 ...
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0answers
17 views

Confidence intervals for the Log Loss metric for model comparison?

Quite a few Kaggle competitions have used or are using the Logarithmic Loss metric as the quality measure of a submission. I'm wondering if there are other ways besides N-fold cross-validation to ...
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18 views

Different variable importance results with stabsel and mboost

I'm using glmboost in the mboost package to fit a boosted regression using linear models as the base learner. There are 13200 observations and about 75 variables, ...
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1answer
30 views

Risk Ratio (or Prevalence Ratio) in SAS Proc Glimmix

Is it possible to obtain risk ratio in proc glimmix. And if yes, how do I specify the base. E.g base 'male' in variable 'gender'. Below is a template of my model: ...
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41 views

Quantifying the predictive ability of a model developed from a huge data set? (variation of bootstrapping?)

I have a statistical model with around 20 predictor variables, built on 90% of a dataset consisting of over 600k observations. The original developer held out 10% of the original dataset for the ...
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18 views

Assessing predictor contribution to model output

Many of machine learning methods are considered as "black boxes". Examples of such methods are SVM, Neural Networks, Random forests etc. One may apply sensitivity analysis techniques (as described for ...
2
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2answers
56 views

Predictive with uniform likelihood

I'm trying to get a predictive density and currently getting something which I know can't be true (based on both logic and simulation based techniques. Here's the relevant information. $\theta$ is a ...
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0answers
21 views

Transformations in Simple Linear Regression [duplicate]

Suppose a linear model for Y in a single predictor var, X. If the residuals show a pattern of increasing variance (wrt X), sometimes a transformation of Y, Y'=f(Y) is considered (where f is sq rt, ...
3
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2answers
42 views

What is the main idea behind the power spectrum?

Assume that we have a time series and we have calculate the corresponding auto-covariance function. Having the auto-covariance function we can calculate the corresponding power spectrum and having the ...
2
votes
1answer
41 views

How to adjust data to remove influence of one or more features

For my first real data science project I would like to develop a model which better reflects review quality than "useful" votes. I am working with Yelp's latest Academic data set but this thinking ...
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33 views

Fitting a model to data for prediction - best choice for data

I have some data I need to fit a model to that can be used for prediction (interpolation). The data is summarized by the plot below. The black line is x=y. I want to be able to fit a model so as I ...
2
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1answer
54 views

c-index for parametric links in binary regression

I am conducting a binary regression using different sorts of parametric links (logistic, Pregibon, Aranda-Ordaz, ... see) and I would like to compare their predictive and classification perfomance in ...
0
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1answer
13 views

Predictive analysis based on history

Let me first say that I am a CS person and my knowledge about statistics is quite basic. I am trying to see what predictive analysis to use for a problem I am trying to solve. I will try to make my ...
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2answers
33 views

Predict Seasonal Variations

I am developing an application related to pharmaceutical industry. Certain items are sold in significantly higher quantities during specific periods of the year. For example, here in my country, ...