Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining. 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

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. We can now pass the preprocessed data to the Pumas NCAReport function, which calculates a wide range of relevant NCA metrics. We can merge all the metrics in a separate DataFrame for further analysis.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. 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. return synthetic.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Conference on Knowledge Discovery and Data Mining, pp.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139