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Fundamentals of Data Mining

Data Science 101

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The models created using these algorithms could be evaluated against appropriate metrics to verify the model’s credibility. Data Mining Models. Classification.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data. No doubt we have metrics which we track to determine which experimental change is worth launching. In addition to a suitable metric, we must also choose our experimental unit. known, equal variances).

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AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. They have different metrics for judging whether some content is interesting or not. Economy.bg: You work with media companies such as the BBC and the Financial Times.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) A rule-learning program in high energy physics event classification. return synthetic.

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LSOS experiments: how I learned to stop worrying and love the variability

The Unofficial Google Data Science Blog

Variance reduction through conditioning Suppose, as an LSOS experimenter, you find that your key metric varies a lot by country and time of day. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect. Obviously, this doesn’t have to be true.