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However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a ...
Mean imputation can be carried out within classes (i.e. categories such as gender), and can be expressed as ^ = ¯ where ^ is the imputed value for record and ¯ is the sample mean of respondent data within some class . This is a special case of generalized regression imputation:
In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.
In these applications, the set of all inputs is some sort of metric space, and the hashing function can be interpreted as a partition of that space into a grid of cells. The table is often an array with two or more indices (called a grid file , grid index , bucket grid , and similar names), and the hash function returns an index tuple .
Such a model is a log-odds or logistic model. Generalized linear models cover all these situations by allowing for response variables that have arbitrary distributions (rather than simply normal distributions ), and for an arbitrary function of the response variable (the link function ) to vary linearly with the predictors (rather than assuming ...
For the decimal formats, the usual encoding of unsigned integers is used. If an operation has a single NaN input and propagates it to the output, the result NaN's payload should be that of the input NaN (this is not always possible for binary formats when the signaling/quiet state is encoded by an is_signaling flag, as explained above). If ...
A GROUP BY statement in SQL specifies that a SQL SELECT statement partitions result rows into groups, based on their values in one or several columns. Typically, grouping is used to apply some sort of aggregate function for each group.
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