Thoughts and Theory

By Joe Schneid at Wikimedia Commons

Most state-of-the-art (SOTA) time series classification methods are limited by high computational complexity. This makes them slow to train on smaller datasets and effectively unusable on large datasets.

Recently, ROCKET (RandOM Convolutional KErnel Transform) has achieved SOTA of accuracy in just a fraction of the time as other SOTA time…

Machine Learning

Image from Wikimedia commons by Tomas Castelazo

Outlier detection is a machine learning task that aims to identify rare items, events, or observations that deviate from the “norm” or general distribution of the given data.

This article is an in-depth guide to both theory and application of the Local Outlier Factor (LOF) algorithm for outlier detection. We…

Opinion

Image by UnboxScience at Pixabay

Conventional machine learning algorithms, such as linear regression and xgboost, operate in “batch” mode. That is, they fit a model using a full dataset in one go. Updating that model with new data requires fitting a brand new model from scratch using both the new data and the old data.

Image by geralt at pixabay

Sktime is a popular new python package for time series machine learning. The contributors continue to fix bugs and add new features — and invite you to contribute too!

Why contribute to sktime?

  1. Improve your skills in machine learning and coding.
  2. Learn the nuts-and-bolts of machine learning algorithms.
  3. Build your…

Machine Learning

Image by Hans at Pixabay

Outlier detection is a machine learning task that aims to identify rare items, events, or observations that deviate from the “norm” or general distribution of the given data.

An anomaly is something that arouses suspicion that it was generated by different data generating mechanism

The Outlier Detection Machine Learning Task

In the outlier detection task, the…

Photo by Anita Ritenour at flickr

PyOD is a Python library with a comprehensive set of scalable, state-of-the-art (SOTA) algorithms for detecting outlying data points in multivariate data. This task is commonly referred to as Outlier Detection or Anomaly Detection.

The outlier detection task aims to identify rare items, events, or observations that deviate from the…

Alexandra Amidon

Data scientist working in the financial services industry

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