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

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. pain_df.TIME.==

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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. Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. Courville, Pascal Vincent, Visualizing Higher-Layer Features of a Deep Network, 2009.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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) Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136