484 research outputs found
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Machine-learned models are often described as "black boxes". In many
real-world applications however, models may have to sacrifice predictive power
in favour of human-interpretability. When this is the case, feature engineering
becomes a crucial task, which requires significant and time-consuming human
effort. Whilst some features are inherently static, representing properties
that cannot be influenced (e.g., the age of an individual), others capture
characteristics that could be adjusted (e.g., the daily amount of carbohydrates
taken). Nonetheless, once a model is learned from the data, each prediction it
makes on new instances is irreversible - assuming every instance to be a static
point located in the chosen feature space. There are many circumstances however
where it is important to understand (i) why a model outputs a certain
prediction on a given instance, (ii) which adjustable features of that instance
should be modified, and finally (iii) how to alter such a prediction when the
mutated instance is input back to the model. In this paper, we present a
technique that exploits the internals of a tree-based ensemble classifier to
offer recommendations for transforming true negative instances into positively
predicted ones. We demonstrate the validity of our approach using an online
advertising application. First, we design a Random Forest classifier that
effectively separates between two types of ads: low (negative) and high
(positive) quality ads (instances). Then, we introduce an algorithm that
provides recommendations that aim to transform a low quality ad (negative
instance) into a high quality one (positive instance). Finally, we evaluate our
approach on a subset of the active inventory of a large ad network, Yahoo
Gemini.Comment: 10 pages, KDD 201
You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction
In the cost per click (CPC) pricing model, an advertiser pays an ad network
only when a user clicks on an ad; in turn, the ad network gives a share of that
revenue to the publisher where the ad was impressed. Still, advertisers may be
unsatisfied with ad networks charging them for "valueless" clicks, or so-called
accidental clicks. [...] Charging advertisers for such clicks is detrimental in
the long term as the advertiser may decide to run their campaigns on other ad
networks. In addition, machine-learned click models trained to predict which ad
will bring the highest revenue may overestimate an ad click-through rate, and
as a consequence negatively impacting revenue for both the ad network and the
publisher. In this work, we propose a data-driven method to detect accidental
clicks from the perspective of the ad network. We collect observations of time
spent by users on a large set of ad landing pages - i.e., dwell time. We notice
that the majority of per-ad distributions of dwell time fit to a mixture of
distributions, where each component may correspond to a particular type of
clicks, the first one being accidental. We then estimate dwell time thresholds
of accidental clicks from that component. Using our method to identify
accidental clicks, we then propose a technique that smoothly discounts the
advertiser's cost of accidental clicks at billing time. Experiments conducted
on a large dataset of ads served on Yahoo mobile apps confirm that our
thresholds are stable over time, and revenue loss in the short term is
marginal. We also compare the performance of an existing machine-learned click
model trained on all ad clicks with that of the same model trained only on
non-accidental clicks. There, we observe an increase in both ad click-through
rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when
using the latter. [...
Vertex adjacencies in the set covering polyhedron
We describe the adjacency of vertices of the (unbounded version of the) set
covering polyhedron, in a similar way to the description given by Chvatal for
the stable set polytope. We find a sufficient condition for adjacency, and
characterize it with similar conditions in the case where the underlying matrix
is row circular. We apply our findings to show a new infinite family of
minimally nonideal matrices.Comment: Minor revision, 22 pages, 3 figure
Rischio sismico Padiglione 5 nell'Ospedale di Livorno
Nella presente tesi ho analizzato il padiglione 5 degli Spedali Riuniti di Livorno sia dal punto di vista gravitazionale che sismico, effettuando sia l'analisi lineare che non lineare(pushover).Il passo successivo è stata l'analisi cinematica, lineare e non lineare dei cinematismi presenti nella struttura. In fine è stato valutato il nuovo tempo di ritorno sismico per cui la l'edificio verifica nell'analisi non lineare
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