0
votes
0answers
6 views

Building a predictive model, regression with a long right tail

I am trying to build a, regressive, predictive model for a target time-series that is heavily skewed. You could think of the target as being like earthquake magnitudes or heavy rainfall. Most of the ...
0
votes
0answers
11 views

algorithm to predict cost function

The goal of problem is to predict the weight for missing data . I have a dataset of categorical type as shown below ...
0
votes
0answers
32 views

Stochastic Gradient Descent for Logistic Regression always returns a cost of Inf and weight vector never gets any closer

I am trying to implement a logistic regression solver in MATLAB and i am finding the weights by stochastic gradient descent. I am running into a problem where my data seems to produce an infinite ...
1
vote
0answers
17 views

Using standard machine learning tools on left-censored data

I'm developing a forecasting application whose purpose is to allow an importer to forecast demand for its products from its customer network of distributors. Sales figures are a pretty good proxy for ...
0
votes
0answers
17 views

How to compute F-statistics for each features of regression models in glmnet?

I have learned lot's of Lasso regression models(20000) using glmnet. I need to compute somehow test statistics for each features of models. like F-statistics,... Can I do this using bootstrapping ? ...
0
votes
0answers
16 views

Multiple Class Logistic Regression Coefficents unstable when classes well separated

The following is a quote about the performance of Logistic Regression on multiple classes when the individual classes are well separated by the book ISLR: "When the classes are well-separated, the ...
0
votes
0answers
19 views

Why would a reasonable range of the regularization parameters $\lambda$ be up to the maximum eigenvalue of the kernel matrix?

I was wondering, how do you choose a reasonable range for the regularization parameter $\lambda$ for regularized least squares when doing k-fold cross validation? I was told that a reasonable range ...
2
votes
1answer
42 views

Maximum Degree of Polynomial Regression

If we have 100 data points and want to perform polynomial regression, the maximum degree of our polynomial is n-1, where n is the number of data points. In this case, the maximum degree would be 99. I ...
0
votes
0answers
30 views

Change in objective function optimization - Regularization in Logistic Regression

If I have the objective function of Logistic Regression to optimize by maximizing it, would it change to a minimization problem when I add regularization term to it? Or can I still solve the ...
0
votes
0answers
18 views

SQL query optimization using machine learning

This is related to my thesis work. I am trying to use SVM for query optimization. After finding the best query plan, I have to train the machine so that whenever the same type of query appears the ...
3
votes
3answers
110 views

Understanding Gaussian Basis function parameters to be used in linear regression

I'd like to apply the Gaussian basis function into a linear regression implementation. Unfortunately I'm having a hard time understanding a couple parameters in the basis function. Specifically mu ...
0
votes
0answers
27 views

How to calculate multicollinearity of binary variable with other predictors in regression model?

VIF can be used to calculate multicollinearity of continuous variable in regression models. But VIF will only work for continuous variables because this is calculated by running a linear regression ...
1
vote
0answers
45 views

Dichotomizing Continuous Variables in Regression: Good or Bad? [duplicate]

I believe Dichotomizing(also called bucketing/binning) of continuous variable is not always a good idea. My colleague while building regression model always bins continuous variables and only keep ...
0
votes
0answers
47 views

Random variables of mixed models

I am thinking about using mixed models as part of my research, but I am having trouble understanding its application. In particular, I have two somewhat related questions regarding mixed models. ...
0
votes
0answers
21 views

Live selection of movie to suggest based on similarity of users

I am working with movie selection for users. 1 ) One of the first ways I thought was taking all the clicked only movie data and building decision trees out of it. Then when input is passed, the ...
1
vote
1answer
46 views

Algorithm to find subsets with high correlation

I have a reasonably large dataset (d) with predictor variables x1...xn and a target variable y. I can use recursive partitioning (such as CART or rpart in R) to find subsets of d with a high (or low) ...
2
votes
1answer
26 views

What kind of functions can have non whole degrees?

Thanks for the help in advance. I am reading a technical report on a regression algorithm that reports a pair of functions as having a total degree of freedom of 5.4. I believe that both of these ...
1
vote
0answers
20 views

Hard Case - prediction of chain stores revenue

Data about average monthly revenue from 2000 stores around whole country. Gini coeff. of reve around 20%, with 50% of observation around average, very thin tails of distribution Explanatory ...
2
votes
0answers
18 views

mob model tree algorithm

I am trying to figure out the inner workings of the mob function in the party package. I can't figure out how the splitting variable is selected when it is a categorical variable. In the publications ...
0
votes
0answers
33 views

Financial time series model

I have an interesting question that I think has not been asked yet here. I am building an AI that has as goal to predict how wrong a standard based-on-history model is. This is done based on Natural ...
1
vote
1answer
63 views

Linear regression of 0/1 response (Fig. 2.1 of The elements of statistical learning)

In chapter 2 ESL book authors write: Let's look at example of linear model in a classification context They fit a simple linear model $g = 0.3290614 -0.0226360\cdot x_1 + 0.2495983 \cdot x_2 + e$, ...
0
votes
0answers
14 views

Is time-delay embedding/attractor reconstruction used in some machine learning algorithm?

I try to model/forecast blood glucose levels from my diabetes diary, so I have to deal with some 5-7 daily measurements of estimated carbohydrates, physical activity, insulin doses and measured blood ...
0
votes
3answers
106 views

How to Use Neural Networks to Forecast Time Series Data with Predictor Variables?

I have browsed a lot of topics here, but the ones I see were all about forecasting a single variable, depending on its historical values. Whereas I want to predict a variable, by estimating a ...
0
votes
0answers
27 views

Stacked Generalization Ensemble Algorithm for regression

I am using stacked generalization(Rupert 1992) for combining multiple(8) heterogeneous base learners for regression. What I understand from the pseudo codes that Train the 8 learners on 8 instances ...
3
votes
2answers
100 views

How does random Forest work for regression?

I am an absolute beginner in field of machine learning, I started doing titanic assignment in Kaggle and found(read some where) Random Forest is the best fit. I started reading about random forest and ...
0
votes
1answer
18 views

How to use a set attributes of an entity at different time snaps to make predictive analysis?

The problem is to come up with a classifier for any task based on a set of attributes of an entity having different values at different times. For instance think about football players and their match ...
2
votes
2answers
58 views

Which property of count data make mean-variance dependency?

I have read about the fact that, there is dependency of variance on mean of count data.In most of cases they do variance stabilization transfomration as preprocessing step of data modeling. I wonder, ...
1
vote
3answers
83 views

Is it necessary to scale the target value in addition to scaling features for regression analysis?

I'm building regression models. As a preprocessing step, I scale my feature values to have mean 0 and standard deviation 1. Is it necessary to normalize the target values also?
0
votes
0answers
33 views

What is task-loss function?

I looked into "Multi-Output Learning for Camera Relocalization" research and faced with the following part (2.2 The Direct Regression Approach): Given a set of RGB-D frames with known camera poses ...
2
votes
1answer
25 views

How to isolate impact of event in a product's lifecycle?

I'm trying to figure out how a single event affects sales numbers of a song. For example, see what the effect of being featured in iTunes store compared to songs with comparable previous download ...
0
votes
1answer
65 views

Why, when I scale my data set, glmnet gives error?

I'm using glmnet for building the regression models. My data are already log-transformed. when I scale my data set (zero mean, and SD=1), I get the following error: ...
0
votes
0answers
52 views

Prediction using Support Vector (SV) method in R

I came to know that using SVM method we can predict the future value more accurately than other normal methods (like ARIMA). My question is how do we give the future index value (let's say 101 when we ...
9
votes
2answers
648 views

What does the name “Logistic Regression” mean?

I am checking an implementation of Logistic Regression from here. After I reading that article, it seems the important part is the find the best coefficients to determine the sigmoid function. So I ...
2
votes
2answers
94 views

How to build a prediction model for exam score based on previous scores

I am trying to construct a formula, which will take student's previous exam results (for ex: SAT) taken at particular dates and predict his future test result. One X is previous test result 1; ...
2
votes
1answer
47 views

Model for probability of song reaching top 10 ranking, over time?

I'm trying to model the probability of a song reaching Billboards top 10 over time. My data has the columns "Day since release", "If reached top 10". For example, [12,1] means the song hit top 10 on ...
1
vote
0answers
31 views

PCA + L1 equivalent to elastic net?

I am performing a logistic regression on a rather big dataset (700k+ samples and 1k+ features). I suspect that a lot of these features will be highly correlated and multicollinearity can be an issue. ...
4
votes
2answers
70 views

When do kernel based method perform better than the regular

I am used with linear models. I can see rising use of kernel based method particularly in machine learning. The following is an example Gaussian kernel using ...
0
votes
0answers
37 views

Neural-Net style pattern recognition with an unknown/varying number of inputs?

Say for example I had a weighted graph such that each node had an associated value. The nodes' values are given by some function of the edge weights and the number of edges as well as the node's ...
3
votes
2answers
92 views

Good machine learning algo for partial derivatives?

Does anyone know of good robust algos to estimate partial derivatives of a regression model? I am talking about a general regression model like this: $\mathbb{E}(y|x_1, x_2, ... x_n) = f(x_1, x_2, ...
0
votes
0answers
21 views

how to implement linear or non linear regression for 3d position estimation?

I am a beginner in Machine Learning. For my project I need a regression algorithm that can estimate the 3D position of a device based on some constraints (moreover inputs). I know how to implement ...
2
votes
1answer
67 views

For a model like this what performance measures can I calculate and how?

Methods: From the machine learning literature, I understand different parameters can show performance of model in machine learning. I would briefly expand my understanding with confusion matrix: ...
1
vote
0answers
17 views

How to assess the importance of the features which come from intersection of features of the two models?

I have two models from two different data sets. Model 1 contain 50 features and model 2 contain 40 features. the intersection of features of model 1 and 2 is 10. so how can I assess the relative ...
0
votes
1answer
18 views

How to compare the nested models which each of them comes from diffrent dataset?

I have four nested models.Every of them learned from different data sets. now I want to compare these models together.normally people try to compute the F-satistics. But for my case, it's bit harder, ...
3
votes
2answers
40 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
0
votes
1answer
75 views

How to compare models from different but related datasets?

I'm building regression models on four the different but related data set and at the end, I want to test the significance of models. Since my models are built in a different data set, it's not ...
1
vote
1answer
19 views

Regression Gaussian Estimates instead of points

I am using a support vector regression is order to get estimates of a variable y. I want to receive a probability distribution of my estimates and not just point estimates. I want to predict Gaussian ...
1
vote
0answers
31 views

Binary classification of dated text documents with seasonality

I have a collection of training documents with publication dates, where each document is labeled as belonging (or not) to some topic T. I want to train a model that will predict for a new document ...
0
votes
0answers
41 views

Conditional or Joint Probability under Various distributions

In various statistical models the baseline equation (like in Naive Bayes $$\mathrm{classify}(f_1,\dots,f_n) = \underset{c}{\operatorname{argmax}} \ p(C=c) \displaystyle\prod_{i=1}^n p(F_i=f_i\vert ...
0
votes
0answers
20 views

Regression line fit for linearly increasing data with manual reset

I've a linearly increasing time series dataset of a sensor with value ranges between 50 to 150 on which I've implemented a simple linear regression algorithm to fit a regression line, and I'm ...
2
votes
2answers
472 views

Estimate ARMA coefficients through ACF and PACF inspection

I know that this is probably a question that's been asked plenty of times, but i haven't seen an answer that's both accurate and simple. How do you estimate the appropriate forecast model for a time ...