Download CSV. 2737 Downloads: Census Income. Predict if an individual makes greater or less than $50000 per year. Instances: 48842, Attributes: 15, Tasks: Classification. Download CSV. 2672 Downloads: German Credit Data. Determine customer credit rating (good vs bad). % example.) The data set contains 3 classes of 50 instances each,% where each class refers to a type of iris plant. One class is% linearly separable from the other 2; the latter are NOT linearly% separable from each other.% - Predicted attribute: class of iris plant.% - This is an exceedingly simple domain.%% 5. A zip file containing a new, image-based version of the classic iris data, with 50 images for each of the three species of iris. The images have size 600x600. Please see the ARFF file for further information (irisreloaded.zip, 92,267,000 Bytes). After expanding into a directory using your jar utility (or an archive program that handles tar. The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. Here is a link to the German Credit data (right-click and 'save as'). A predictive model developed on this data is expected to provide a bank manager guidance for making a decision. Mar 18, 2016 Here this model is (slightly) better than the logistic regression. Actually, if we create many training/validation samples, and compare the AUC, we can observe that – on average – random forests perform better than logistic regressions.
In our data science course, this morning, we’ve use random forrest to improve prediction on the German Credit Dataset. The dataset is
Almost all variables are treated a numeric, but actually, most of them are factors,
(etc). Let us convert categorical variables as factors,
Let us now create our training/calibration and validation/testing datasets, with proportion 1/3-2/3
The first model we can fit is a logistic regression, on selected covariates
Based on that model, it is possible to draw the ROC curve, and to compute the AUC (on ne validation dataset)
An alternative is to consider a logistic regression on all explanatory variables
We might overfit, here, and we should observe that on the ROC curve
There is a slight improvement here, compared with the previous model, where only five explanatory variables were considered.
Consider now some regression tree (on all covariates)
We can visualize the tree using
The ROC curve for that model is
As expected, a single has a lower performance, compared with a logistic regression. And a natural idea is to grow several trees using some boostrap procedure, and then to agregate those predictions.
Here this model is (slightly) better than the logistic regression. Actually, if we create many training/validation samples, and compare the AUC, we can observe that – on average – random forests perform better than logistic regressions,
The dataset contains data of past credit applicants. The applicants are ratedas good or bad. Models of this data can be used to determine ifnew applicants present a good or bad credit risk.
The use of a cost matrix is suggested for this dataset. It is worse to class a customer as good when they are bad (cost = 5), than it is to class a customer as bad when they are good (cost = 1).
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A data frame containing 1,000 observations on 21 variables.
factor variable indicating the status of the existing checking account, with levels
.. < 0 DM,
0 <= .. < 200 DM,
.. >= 200 DM/salary for at least 1 year and
no checking account.
duration in months.
factor variable indicating credit history, with levels
no credits taken/all credits paid back duly,
all credits at this bank paid back duly,
existing credits paid back duly till now,
delay in paying off in the past and
critical account/other credits existing.
factor variable indicating the credit's purpose, with levels
factor. savings account/bonds, with levels
.. < 100 DM,
100 <= .. < 500 DM,
500 <= .. < 1000 DM,
.. >= 1000 DM and
unknown/no savings account.
ordered factor indicating the duration of the current employment, with levels
.. < 1 year,
1 <= .. < 4 years,
4 <= .. < 7 years and
.. >= 7 years.
installment rate in percentage of disposable income.
factor variable indicating personal status and sex, with levels
factor. Other debtors, with levels
present residence since?
factor variable indicating the client's highest valued property, with levels
building society savings agreement/life insurance,
car or other and
factor variable indicating other installment plans, with levels
none. Digital prism 3 in 1 photo converter driver for mac torrent.
factor variable indicating housing, with levels
number of existing credits at this bank.
factor indicating employment status, with levels
unemployed/unskilled - non-resident,
unskilled - resident,
skilled employee/official and
management/self-employed/highly qualified employee/officer.
Number of people being liable to provide maintenance.
binary variable indicating if the customer has a registered telephone number.
binary variable indicating if the customer is a foreign worker.
binary variable indicating credit risk, with levels