Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The choice of these metrics depends on the nature of the problem.

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Data mining for direct marketing: Problems and solutions. return synthetic. References.

Machine Learning 136

Machine Learning Metrics Data mining Data Science 136

Domino Data Lab

DECEMBER 4, 2020

Approximating the region under the graph of as a series of trapezoids and calculating the sum of their area (in the case of non-uniformly distributed data points) is given by. Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. Mean residence time. and many others.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. Henne, Dan Sommerfield, Overall Evaluation Criterion , Proceedings 13th Conference on Knowledge Discovery and Data Mining, 2007. 2] Ron Kohavi, Randal M.

Modeling 63

Modeling Experimentation Knowledge Discovery Testing 63

Domino Data Lab

AUGUST 1, 2021

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. In this post we explore how and why we can be “ data-rich but information-poor ”. There are many reasons for the recent explosion of data and the resulting rise of data science.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Variance reduction through conditioning Suppose, as an LSOS experimenter, you find that your key metric varies a lot by country and time of day. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect. Obviously, this doesn’t have to be true.

Experimentation 41

Experimentation Statistics Metrics Measurement 41