Data Science Austria

Why feature weights in a machine learning model are meaningless

Don’t make decisions based on the weights of an ML model Aug 31, 2018 As I see our customers fall in love with BigQuery ML, an old problem rises its head — I find that they can not resist the temptation to assign meaning to feature weights. “The largest weight in my model … Read moreWhy feature weights in a machine learning model are meaningless

Doing XGBoost hyper-parameter tuning the smart way — Part 1 of 2

Aug 29, 2018 Picture taken from Pixabay In this post and the next, we will look at one of the trickiest and most critical problems in Machine Learning (ML): Hyper-parameter tuning. After reviewing what hyper-parameters, or hyper-params for short, are and how they differ from plain vanilla learnable parameters, we introduce … Read moreDoing XGBoost hyper-parameter tuning the smart way — Part 1 of 2

Automatic Image Quality Assessment in Python

Aug 28, 2018 Image quality is a notion that highly depends on observers. Generally, it is linked to the conditions in which it is viewed; therefore, it is a highly subjective topic. Image quality assessment aims to quantitatively represent the human perception of quality. These metrics are commonly used to analyze … Read moreAutomatic Image Quality Assessment in Python

Neural Processes: Probabilistic Gaussian Process+Deep Learning

Neural Processes (NPs) caught my attention as they essentially are a neural network (NN) based probabilistic model which can represent a distribution over stochastic processes. So NPs combine elements from two worlds:

Deep Learning – neural networks are flexible non-linear functions which are straightforward to train
Gaussian Processes – GPs offer a probabilistic framework for learning a distribution over a wide class of non-linear functions

Mapping the UK’s Traffic Accident Hotspots

While looking for some interesting geographical data to work with, I came across the Road Safety Data published by the UK government. This is a very comprehensive road accident data set that includes the incident’s geographical coordinates, as well as other related data such as the local weather conditions, visibility, … Read moreMapping the UK’s Traffic Accident Hotspots

Forecasting Time-series with Multiple Seasonal Patterns

Forecasting time-series which contain multiple seasonal patterns requires flexible modelling approaches, and the need for continuously updating models emphasises the importance of fast model estimation. In response to shortcomings in current models, a new model is proposed which brings the desirable qualities of speed, flexibility and support for exogenous regressors into a state space model.