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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Crimes predictive Model

I am working on developing an insurance risk predictive model. I don't have a lot of experience in statistics and modeling data beyond a high school statistics course so I'm kinda confused. at ...
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5 views

Calculating variable importance at the individual prediction level

I'm trying to build a model which shows the significance of each feature in contributing to the score of each individual prediction. As a dirty hack, I'm running a logistic regression with ...
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1answer
11 views

Is it possible prediction of chemical activity with few data?

I have activity data (represented by a real number) for five chemical compounds (and for which I have a set of 600 descriptors) and would like to use neural networks or SVM or any other system that ...
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1answer
33 views

How do we predict rare events?

I am working on developing an insurance risk predictive model. These models are of "rare events" like airline no-show prediction, hardware fault detection, etc. As I prepared my data set, I tried to ...
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20 views

Are trend/cycle filters intended to be used in predictive models, or just analysis?

I am relatively new to time series modelling and for a task I have I've had good success (in terms of forecast error) by first splitting the data into a trend and cycle components using a ...
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30 views

Neural net model - error during training

I'm getting started with R, I really like it but recently I found myself in a corner. I'd like to build neural network model that predicts heat consumption. I have historical data that contains ...
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41 views

How to gently introduce epidemiologists/public health coworkers to advanced predictive modeling?

Coming from a social science and epidemiology background, my coworkers were trained on least squares regression, logistic regression, and survival analysis. They like to see 95% confidence intervals ...
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21 views

Finding what change in each variables equates to the same impact

I hope this is even possible. I'm doing some stats on a fantasy basketball league I am in. Let's assume I have the totaled stats for the week for each team in a pandas dataframe. It looks something ...
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22 views

Literature for prediction models where each training example has a different amount of data?

This could be a machine learning question as much as a statistics question, but I think this is the best place to put the question. Here are three different examples of problems where each ...
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27 views

How to model to improve the room usage efficiency based on motion sensor history

To reduce the confusion, I changed my application from traffic to meeting room, so this application is about modeling a meeting room efficiency , the data collection is built by placing a kind of ...
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11 views

Predictive Model with Underlying Environment Trending

I'm looking for some ideas / perspectives on what can be done when a predictive model is being trained on sample data that is taken over a period of time and the underlying environment is changing ...
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1answer
35 views

Cost Benefit Analysis of Pre-screening Widgets for Faults before they Fail

I want to build a model that determines whether to pre-screen my widgets for defects. If I do pre-screen, it costs a fixed amount per check and I resolve the problem 100% of the time. If I don't ...
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1answer
67 views

R: plotting a prediction model and understanding results

I am experimenting with developing a linear model lmodel1 which predicts a temp response variable, given three independent ...
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8 views

Predicting the growth of a social network

I am building a predictive model for the growth of the amount of users of a new p2p protocol inspired by bitcoin and I would like to use historical data collected from the growth of major social ...
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1answer
52 views

R: multiple linear regression model and prediction model

Starting from a linear model1 = lm(temp~alt+sdist) i need to develop a prediction model, where new data will come in hand and predictions about ...
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1answer
48 views

Suggest models for prediction based on small sample data

I am not a traditional statistics guy. I am from an electrical engineering background. So, spare me for lack of jargon. The model is to be used for predicting agricultural output based on previous ...
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2answers
23 views

How to determine the factors correlated with observed data?

I have box-office collection data on a number of movies. I also have the production budget, director name, lead actor, actress, language and other meta data related to the movie. I want to know which ...
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11 views

How to calculate performance statistics of continus learning model?

I have continuous weak stationary process that I need to map on logical result value (0,1). For example I want model that in 2 ways: logistic regression native Bayes classificator I want to know ...
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79 views

How many people bought wine?

Rephrased a problem trying to solve for work in terms of people buying wine, also included progress made so far. Set-up: Customers enter a winery with the option of buying bottles of wine. Those who ...
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15 views

Which method(s) for forecasting time series of event durations

I have the $N$ individuals each observed for $T$ days. For each individual I have some basic demographic data. Each $n$ individual, during the observed time $T_n$ may experience event $E$ which is ...
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2answers
84 views

How to do external validation of logistic regression models and perform model benchmarking

Quality assessment in trauma has for > 25 years been done with the US derived logistic regression model, the TRISS model. DV: survival/death and IVs: physiologic derangement (continuous), anatomic ...
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2answers
104 views

Are there any probabilistic models for graph-based recommender systems?

All I can find now is somehow based on random walks or graph kernels, which is nice, but I want to have a more or less solid probabilistic foundation for my recommender system for bounds and ...
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7 views

Feature importance and interpretation of alternating decision trees

Is there a way of calculating feature importance in alternating decision trees? What if I've already trained an alternating decision tree and want to calculate feature importance in terms of ...
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45 views

ARIMAX for modelling daily sales

I am trying to model daily sales for a take out restaurant. They are only open on business days - no holidays or weekends - as their primary clients are office workers on their lunch breaks. Below is ...
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19 views

building a feature set for scikit learn

Im using RandomForestClassifier for a probability prediction task. I have a featureset of around 50 features and two possible labels - ...
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10 views

Statistical analysis and prediction of variations in anatomical features

I am doing a statistical analysis of variations in anatomical features, for example the shape of a pelvis bone. I found out that morphometric method can be used to quantify differences in geometry ...
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18 views

Time Series Forecasting Method to use both Predicted and Predictor variables

I am learning Predictive modeling and building a Forecasting model to predict Insurance sales in US as a part of my academic project. I want to do Time Series forecasting. I have Y(t) as my response ...
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11 views

Bias in predicting survival with time varying covariates

I'm trying to predict survival probabilities with time-varying covariates. My dataset constitutes a variety of subjects who enter the study at different dates and receive multiple follow-ups. For each ...
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1answer
72 views

predict function and categorical variables in R

This is more of a general question about how the predict function treats categorical variables and how to interpret the output from predict. I have a zeroinfl model to predict the number of animals ...
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1answer
111 views

Time series prediction: visualising path uncertainty region

I am predicting a time series' future evolution and am evaluating the path uncertainty using bootstrapping. Is there a good way to visualise the uncertainty that goes beyond simply plotting a pair of ...
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72 views

Short term road traffic forecast modeling using neural networks toolbox in matlab

I have a hourly time series data of road traffic (i.e. count of the number of vehicles passing on a particular segment of road) collected over 7 days a week (Mon to Sun) for two weeks starting from 9 ...
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1answer
74 views

Discrepancy between log likelihood Harrell's C Index and brier score for the evaluation of a Cox regression

I am evaluating a dataset of ~400 subjects and 10 covariates trying to fit a Cox ph model for predicting survival in AML patients. To evaluate the models I am using a bootstrapping procedure of 50 ...
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27 views

Building prediction model with estimated predictor variables

I'm planning to use logistic regression with multiple (~5) predictor variables to predict whether something happens or not. I have two types of predictor variables: known (measurable) variables and ...
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37 views

Prediction with categorical and numeric variables

I used R to estimate a regression with both numeric and categorical variables, and obtained coefficient estimates. However, when I try to make predictions using new data, there appear to be some ...
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34 views

Data mining of time series

I have a dataset which consist of n time series variables $X_1$..$X_n$ , and a time serie output $Y$. I would like to mine the data to find if some functions (lagged or not) of the $X_i$ can predict ...
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30 views

Comparing 2D heat maps of observed data to 2D model predictions

From "How to ask a statistics question": PROBLEM you are trying to solve: Given two-dimensional heat maps of responses (DV), choose the 2D model (also a heat map, but can have different ranges of ...
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2answers
35 views

Selecting features and estimating their out-of-sample performance with cross-validation

I have only a small dataset. I want to 1. select the most predictive features out of a large candidate pool and 2. get an estimate of their expected predictive performance. In the elements of ...
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2answers
215 views

Cross-validation and feature selection of a multivariate regression

I've been trying to create a multivariate regression model to fit my training data into the prediction of a value. I've put my data into a matrix X with ...
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1answer
75 views

Predicting class probabilities in regression based on area under the curve

Logistic regression models the log odds. That is for rv $Y$ which is binary logit$(Y=1)=X\beta$. Then with this model, you can estimate the class probabilities and hence prediction or ...
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27 views

Financial Time series prediction/ SV Regression

I'm working with R software (Lib e1071) and I'm trying to get predictions using Support Vector Regression. The way I'm doing it is the following: I'm windowizing the raw closing prices using N=3 ...
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33 views

Finding the equation for a data set

I asked this question on StackOverflow and someone pointed for me to ask this here. Lets assume that I have 3 variables: x, y, z which are integers. I have 5000 values of each and I want to predict ...
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46 views

Time Period Predictive Modeling

I have been implementing some classification algorithms (Naive Bayes, SVM etc) recently on the iris data sets to get head start into the data science field. I enjoy working on machine learning ...
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53 views

Two-day Music chart predictions and the serial correlation effect on the prediction

I have about 64000 music Charts ranked by their usage frequency. I want to have a future two-day prediction frequency and eventually its rank for each music chart using its past 21 days usage ...
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1answer
23 views

Tools for weak relationships

What are the possible data analysis tools that may be sensitive to weak relationships? I intent to explore relationship (if any) between weather and employee absenteeism. However the "traditional" ...
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1answer
74 views

Predicting a continuous outcome using point process descriptors

I have measured a series of times for discrete events along with a continuous variable. So essentially I measure a point process $P: t_1, t_2, \dots, t_n$ and values $A_1(t=x_1), A_2(t=x_2), \dots, ...
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0answers
19 views

Proper way to determine attribute feature selection's smaller subset based on result metrics

Overview My goal is to predict survival of an instance for five different time periods (binary attribute). I have a 100,000-instance dataset with 40 attributes and I want to reduce the attributes ...
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43 views

Which confidence interval should I use to bound actual values around a predicted value?

I have a 'black box' model that outputs model predictions, but I don't know what actually goes on inside the model. I also have a bunch of data that I can pass through the model so that I can compare ...
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1answer
61 views

Fitting distribution to spatial data

Cross posting my question from mathoverflow to find some stats specific help. I am studying a physical process generating data which projects nicely into two dimensions with non-negative values. ...
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1answer
68 views

Principles of Time Series Clustering

I would like to understand complexity of time series clustering. Clustering is similarity based, so as a basic step we evaluate distance between to points in a multidimensional space. In time series, ...
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1answer
263 views

Interpretation of a WEKA result buffer - confusion matrix and performance

I want to know how to get several performance measurements of a generated WEKA model. Note that I am predicting a two-class variable, Alive or ...