2009, Data mining, Metrics and Testing

Explaining black-box models using attribute importance, PDPs, and LIME

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. After forming the X and y variables, we split the data into training and test sets. PDPs for the bicycle count prediction model (Molnar, 2009). show_in_notebook().

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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) Their tests are performed using C4.5-generated 1988), E-state data (Hall et al.,

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

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Explaining black-box models using attribute importance, PDPs, and LIME

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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