Machine Learning: Oversampling vs Sample Weighting

How do you "influence" a ML model? For example, imagine a scenario where you'd like to detect anomalies in a given data set. You reach for your favourite algorithm - in my case Isolation Forest: Our example output from Isolation Forest It does fine for most cases, except that one data point which invariably gets … Continue reading Machine Learning: Oversampling vs Sample Weighting

3 uses for random decision trees / forests you (maybe) didn’t know about

Decision tree forests rightly get a lot of attention due to their robust nature, support for high dimensions and easy decipherability. The most well known uses of decision tree forests are: Classification - given a set of samples with certain features, classify the samples into discrete classes which the model has been trained on. Regression … Continue reading 3 uses for random decision trees / forests you (maybe) didn’t know about