Let's say that your dataset is a bunch of temperatures with a bunch of features related to when/where the measurements were made.
A measurement is a global outlier if it diverges from the distribution of temperatures regardless of the features (when and where) because that measurement is far off the global distribution (100°C for example).
The example makes ...
Welcome to the community!
You may want to refer to a tutorial on Agglomerative Hierarchical Clustering before reading this answer. My explanation is more practical.
Assume the data below:
from scipy.cluster.hierarchy import ward, fcluster
from scipy.spatial.distance import pdist
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.text import ...
It sounds like the system can just learn your algorithm for labeling. If that is intended then you can just use that and throw away all the ML. If you want to predict, for example, the diagnosis of icd9=250, then of course there is no point to include icd9 as feature. Alternatively, if there is a history, you can use the record just before the diagnosis of ...
Might be a case of Data leakage.
For 1370 features, 2475 is a very small dataset for such an extreme result.
Please try -
Inspecting the misclassified records.
Try removing the T2DM feature and note the dip
Repeat the last step for all the features. You must observe a negligible dip for other features and a very large dip for any feature which is causing ...
Assuming that these results are obtained on a valid test set with no data leakage, these results don't show overfitting because overfitting would cause great performance on the training set but significantly lower perfomance on the test set.
Make sure that your instances between the training and test set are truly distinct: there might be some data leakage, ...
It pretty much looks like overfitting. It would be also interesting to know which algorythm did you use. Some are really sensitive to low number of instances / big number of features, and yYou have almost so many features as instances.
Trying checking first correlation between features and reduce the number of features with PCA or another method, before ...
A simple, practical approach would be to aggregate your data on each customer. The idea is that the repartition of credit usage / credit limit might not really matter for the overall bankruptcy. You might then want to build new features to avoid loss of information : the number of maxed out credit card, average interest on credit card. This is the general ...
I think this paper just compares algorithms:
If you want something specific, here's the white paper for SLIPPER:
Hope that helps.
This is actually very simple: the more separation you see between the boxes, the stronger the 'predictive power' of the covariate.
Boxplots can be actually viewed as histograms looked at from the top. The box itself encompasses 50% of the data (from 25th to 75th percentile), and the line inside the box is the median. Whiskers show you the bounds of the data, ...
Below are three key differences.
Apromore is a server-side collaborative tool. It is typically installed on a server (on-premise or on the cloud). Users connect to Apromore via the Web browser. Users can upload process models and event logs, organize them into folders, enrich them with log filters and dashboard designs, and share them with other users. ...
In the above case, you can collect annotated data ( which is not seen by the model during training) and validate the predictions made by the model.
And another way is if you are a domain expert or have sufficient knowledge on the data, you can tweak the input values and test the predicted output with your expected output.
Often people confuse between unsupervised feature selection (UFS) and dimensionality reduction (DR) algorithms as the same. For instance, a famous DR algorithm is Principal Component Analysis (PCA) which is often confused as a UFS method! Researchers have suggested that PCA is a feature extraction algorithm and not feature selection because it transforms the ...
You might be interested in this paper and python implementation of various other feature selection for clustering tools and papers:
An excerpt sumarizing the approach:
We address the problem of selecting a subset of important features for clus tering for the whole ...