I am building in Python a credit scorecard using this public dataset: https://www.kaggle.com/sivakrishna3311/delinquency-telecom-dataset
It's a binary classification problem:
Target = 1 -> Good applicant
Target = 0 -> Bad applicant
I only have numeric continuous predictive characteristics.
In the credit industry it is a legal requirement to explain why an applicant got rejected (or didn't even get the maximum score): to meet that requirement, Adverse Codes are produced.
In a classic logistic regression approach, one would do this:
calculate the Weight-of-Evidence (WoE) for each predictive
characteristic (forcing a monotonic relationship between the feature
values and the WoE or log(odds)). In the following example, the
higher the network Age the higher the Weight-of-Evidence (WoE):
replace the data values with the correspondent Weight-of-Evidence.
For example, a value of 250 for Network Age would be replaced by
0.04 (which is the correspondent WoE).
Train a logistic regression
After some linear transformations you'd get something like this:
And therefore it'd be straightforward to assign the Adverse Codes, so that the bin with the maximum score doesn't return an Adverse Code. For example:
Now, I want to train an XGBoost (which typically outperforms a logistic regression on a imbalanced, low noise data). XGBoost are very predictive but need to be explained (typically via SHAP).
What I have read is that in order to make the model decision explainable you must ensure that the monotonic constraints are applied.
Question 1. Does it mean that I need to train the XGBoost on the Weight-of-Evidence transformed data like it's done with the Logistic Regression (see point 2 above)?
Question 2. In Python, the XGBoost package offers the option to set monotonic constraints (via the monotone_constraints option). If I don't transform the data by replacing the Weight-of-Evidence (therefore removing all monotonic constraints) does it still make sense to use "monotone_constraints" in XGboost for a binary problem? I mean, does it make sense to use monotone_constraints with a XGBClassifier at all?
Thanks.
Related
Maybe anyone can help me with this question. I conducted a follow-up study and obviously now have to face missing data. Now I am considering how to impute the missing data at best using MLM in R (f.e. participants concluded the follow up 2 survey, but not the follow up 1 survey, therefore I am missing L1 predictors for my longitudinal analysis).
I read about Multiple Imputation of multilevel data using the pan package (Schafer & Yucel, 2002) and came across the following code:
imp <- panImpute(data, formula = fml, n.burn = 1000, n.iter = 100, m = 5)
Yet, I have troubles understanding it completely. Is there maybe another way to impute missing data in R? Or maybe somebody could illustrate the process of the imputation method a bit more detailed, that would be so great! Do I have to conduct the imputation for every model I built in my MLM? (f.e. when I compared, whether a random intercept versus a random intercept and random slope model fits better for my data, do I have to use the imputation code for every model, or do I use it at the beginning of all my calculations?)
Thank you in advance
Is there maybe another way to impute missing data in R?
There are other packages. mice is the one that I normally use, and it does support multilevel data.
Do I have to conduct the imputation for every model I built in my MLM? (f.e. when I compared, whether a random intercept versus a random intercept and random slope model fits better for my data, do I have to use the imputation code for every model, or do I use it at the beginning of all my calculations?)
You have to specify the imputation model. Basically that means you have to tell the software which variables are predicted by which other variables. Since you are comparing models with the same fixed effect, and only changing the random effects (in particular comparing models with and without random slopes), the imputation model should be the same in both cases. So the workflow is:
perform the imputations;
run the model on all the imputed datasets,
pool the results (typically using Rubin's rules)
So you will need to do this twice, to end up with 2 sets of pooled results - one for each model. The software should provide functionality for doing all of this.
Having said all of that, I would advise against choosing your model based on fit statistics and instead use expert knowledge. If you have strong theoretical reasons for expecting slopes to vary by group, then include random slopes. If not, then don't include them.
Working with a dataset of ~200 observations and a number of variables. Unfortunately, none of the variables are distributed normally. If it possible to extract a data subset where at least one desired variable will be distributed normally? Want to do some statistics after (at least logistic regression).
Any help will be much appreciated,
Phil
If there are just a few observations that skew the distribution of individual variables, and no other reasons speaking against using a particular method (such as logistic regression) on your data, you might want to study the nature of "weird" observations before deciding on which analysis method to use eventually.
I would:
carry out the desired regression analysis (e.g. logistic regression), and as it's always required, carry out residual analysis (Q-Q Normal plot, Tukey-Anscombe plot, Leverage plot, also see here) to check the model assumptions. See whether the residuals are normally distributed (the normal distribution of model residuals is the actual assumption in linear regression, not that each variable is normally distributed, of course you might have e.g. bimodally distributed data if there are differences between groups), see if there are observations which could be regarded as outliers, study them (see e.g. here), and if possible remove them from the final dataset before re-fitting the linear model without outliers.
However, you always have to state which observations were removed, and on what grounds. Maybe the outliers can be explained as errors in data collection?
The issue of whether it's a good idea to remove outliers, or a better idea to use robust methods was discussed here.
as suggested by GuedesBF, you may want to find a test or model method which has no assumption of normality.
Before modelling anything or removing any data, I would always plot the data by treatment / outcome groups, and inspect the presence of missing values. After quickly looking at your dataset, it seems that quite some variables have high levels of missingness, and your variable 15 has a lot of zeros. This can be quite problematic for e.g. linear regression.
Understanding and describing your data in a model-free way (with clever plots, e.g. using ggplot2 and multiple aesthetics) is much better than fitting a model and interpreting p-values when violating model assumptions.
A good start to get an overview of all data, their distribution and pairwise correlation (and if you don't have more than around 20 variables) is to use the psych library and pairs.panels.
dat <- read.delim("~/Downloads/dput.txt", header = F)
library(psych)
psych::pairs.panels(dat[,1:12])
psych::pairs.panels(dat[,13:23])
You can then quickly see the distribution of each variable, and the presence of correlations among each pair of variables. You can tune arguments of that function to use different correlation methods, and different displays. Happy exploratory data analysis :)
My data doesn't contain any zeros. The minimum value for my outcome, y, is 1 and that is the value that is inflated. My objective is to run a truncated and inflated Poisson regression model using R.
I already know how to separate way each regression zero truncated and zero inflated. I want to know how to combine the two conditions into one model.
Thanks for you help.
For zero inflated models or zero-hurdle models, the standard approach is to use pscl package. I also wrote a package fitting that kind of models here but it is not yet mature and fully tested. Unless you have voluminous data, I still recommend you to use pscl that is more flexible, robust and documented.
For zero-truncated models, you can have a look at the VGML::vglm function. You might find useful information here.
Note that you are not doing the same distributional assumption so you won't need the same estimation data. Given the description of your dataset, I think you are looking for a zero-truncated model (since you do not observe zeros). With zero-inflated models, you decompose your observed pattern into zeros generated by a selection model and others generated by a count data model. This doesn't look to be a pattern consistent with your dataset.
Currently i am working on a project whose objective is to find the customer who has more probability to purchase your project.Its a classification model (0 & 1 ).
I have created model with RF and XGB both & calculated gain score ( Data is imbalanced ).Not my more than 80 % customers covering in top 3 decile for training data but when i run the model on validation dataset, it fall back to 56-59 % in both model.
Say i have 20 customers & for better accuracy , i have clustered them, Now model is giving perfect result on cluster 1 customers but perform poor on cluster 2 customers.
Any suggestion to tune the same.
Firstly, if there is a high accuracy difference between your training and validation set your model may suffer from bias. You may need to use a more complex model for this training.
Secondly, because of the imbalance of your dataset, you maybe want to resample the training set. You can use under-sampling or over-sampling techniques(SMOTE).
Thirdly, you may need to use the right evaluation metrics like precision, recall, F1.
Finally, in train/val/test split you need to be careful about the distribution of your dataset. So you can use the stratified keyword to handle this problem.
I have a dataset in which individuals, each belonging to a particular group, repeatedly chose between multiple discrete outcomes.
subID group choice
1 Big A
1 Big B
2 Small B
2 Small B
2 Small C
3 Big A
3 Big B
. . .
. . .
I want to test how group membership influences choice, and want to account for non-independence of observations due to repeated choices being made by the same individuals. In turn, I planned to implement a mixed multinomial regression treating group as a fixed effect and subID as a random effect. It seems that there are a few options for multinomial logits in R, and I'm hoping for some guidance on which may be most easily implemented for this mixed model:
1) multinom - GLM, via nnet, allows the usage of the multinom function. This appears to be a nice, clear, straightforward option... for fixed effect models. However is there a manner to implement random effects with multinom? A previous CV post suggests that multinom is able to handle mixed-effects GLM with poisson distribution and a log link. However, I don't understand (a) why this is the case or (b) the required syntax. Can anyone clarify?
2) mlogit - A fantastic package, with incredibly helpful vignettes. However, the "mixed logit" documentation refers to models that have random effects related to alternative specific covariates (implemented via the rpar argument). My model has no alternative specific variables; I simply want to account for the random intercepts of the participants. Is this possible with mlogit? Is that variance automatically accounted for by setting subID as the id.var when shaping the data to long form with mlogit.data? EDIT: I just found an example of "tricking" mlogit to provide random coefficients for variables that vary across individuals (very bottom here), but I don't quite understand the syntax involved.
3) MCMCglmm is evidently another option. However, as a relative novice with R and someone completely unfamiliar with Bayesian stats, I'm not personally comfortable parsing example syntax of mixed logits with this package, or, even following the syntax, making guesses at priors or other needed arguments.
Any guidance toward the most straightforward approach and its syntax implementation would be thoroughly appreciated. I'm also wondering if the random effect of subID needs to be nested within group (as individuals are members of groups), but that may be a question for CV instead. In any case, many thanks for any insights.
I would recommend the Apollo package by Hess & Palma. It comes with a great documentation and a quite helpful user group.