**Accuracy.** The accuracy reflects the number of times the model is correct.

**Activation function**. This is used within a neural network to transform the input level into an output signal.

**Aggregation.** A process where the data is presented in a summary form, such as average.

**Alternative hypothesis**. Within a hypothesis test, the alternative hypothesis (or research hypothesis) states specific values of the population that are possible when the null hypothesis is rejected.

**Antecedent**. An antecedent is the statement or statements in the IF-part of a rule.

**Applying predictive models**. Once a predictive model has been built, the model can be used or applied to a data set to predict a response variable.

**Artificial neural network**. See neural network.

**Associative rules**. Associative rules (or association rules) result from data mining and present information in the form “if X then Y”.

**Average**. See mean.

**Average linkage**. Average linkage is the average distance between two clusters.

**Backpropagation**. A method for training a neural network by adjusting the weights using errors between the current prediction and the training set.

**Bin**. Usually created at the data preparation step, a variable is often broken up into a series of ranges or bins.

**Binary variable**. A variable with two possible outcomes: true (1) or false (0).

**Binning**. Process of breaking up a variable into a series of ranges.

**Box plot**. Also called a box-and-whisker plot, it is a way of graphically showing the median, quartiles and ...

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