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

Domino Data Lab

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. PDPs for the bicycle count prediction model (Molnar, 2009). Courville, Pascal Vincent, Visualizing Higher-Layer Features of a Deep Network, 2009. Ribeiro, M. Guestrin, C.,

Modeling 139
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

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) The unreasonable effectiveness of data. synthetic = np.empty((N * t, numattrs)) synth_idx = 0.

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Mobile Marketing 2015: Rethink Customer Acquisition, Intent Targeting

Occam's Razor

2009 was the year of mobile. If your company has a non-stinky mobile website and mobile app then congratulations: you have successfully solved the problem of 2009! I'm sure you are impressed at the data mining and intent targeting efforts of TripIt. And travel is by no means unique; try any of your normal brands.

Marketing 144