Contents
Why use K-means for clustering?
Kmeans gives more weight to the bigger clusters. Kmeans assumes spherical shapes of clusters (with radius equal to the distance between the centroid and the furthest data point) and doesn’t work well when clusters are in different shapes such as elliptical clusters.
Is random forest a supervised method?
What Is Random Forest? Random forest is a supervised learning algorithm. The “forest” it builds, is an ensemble of decision trees, usually trained with the “bagging” method.
How to present the feature importance of a random forest classifier?
How to build a random forest classifier, extract the feature importance, and present them beautifully. W hen reporting the results of my machine learning models, I always try to present something more than “I achieved as high as 91% accuracy.” because this one sentence cannot reflect so much work I have done in the modeling process.
How to describe clusters produced via k-means?
I was wondering what the caveats were to the following process for describing clusters produced via K-Means with Random Forests: Train K random forests with a binary output where the label is 1 if that row belongs to the k’th cluster and 0 if it doesn’t
Is the kmean cluster classifier a good evaluation?
However, I was thinking about training a supervised classification model (e.g. random forest classifier) in which cluster numbers are dependent variable and all other features are independent variable. Is using the accuracy of this model a good evaluation of the kmean cluster and why?
Are there any drawbacks to the random forest method?
The drawbacks of the method is to tendency to prefer (select as important) numerical features and categorical features with high cardinality. What is more, in the case of correlated features it can select one of the feature and neglect the importance of the second one (which can lead to wrong conclusions).