Continuize ========== Turns discrete attributes into continuous dummy variables. **Inputs** - Data: input data set **Outputs** - Data: data set with continuized instances The **Continuize** widget receives a data set in the input and outputs the same data set in which the discrete attributes (including binary attributes) are replaced with continuous ones. ![](images/Continuize-stamped.png) 1. [Continuization methods](https://en.wikipedia.org/wiki/Continuity_correction), which define the treatment of multivalued discrete attributes. Say that we have a discrete attribute status with the values low, middle and high, listed in that order. Options for their transformation are: - **First value as base**: the attribute will be transformed into two continuous attributes, status=middle with values 0 or 1 signifying whether the original attribute had value middle on a particular example, and similarly, status=high. Hence, a three-valued attribute is transformed into two continuous attributes, corresponding to all except the first value of the attribute. - **Most frequent value as base**: similar to the above, except that the data is analyzed and the most frequent value is used as a base. So, if most examples have the value middle, the two newly constructed continuous attributes will be status=low and status=high. - **One attribute per value**: this would construct three continuous attributes out of a three-valued discrete one. - **Ignore multinominal attributes**: removes the multinominal attributes from the data. - **Treat as ordinal**: converts the attribute into a continuous attribute with values 0, 1, and 2. - **Divide by number of values**: same as above, except that the values are normalized into range 0-1. So, our case would give values 0, 0.5 and 1. 2. Define the treatment of continuous attributes. You will usually prefer the *Leave them as they are* option. The alternative is *Normalize by span*, which will subtract the lowest value found in the data and divide by the span, so all values will fit into [0, 1]. Finally,*Normalize by standard deviation* subtracts the average and divides by the deviation. 3. Define the treatment of class attributes. Besides leaving it as it is, there are also a couple of options available for multinominal attributes, except for those options which split the attribute into more than one attribute - this obviously cannot be supported since you cannot have more than one class attribute. 4. With *value range*, you can define the values of new attributes. In the above text, we supposed the range *from 0 to 1*. You can change it to *from -1 to 1*. 5. Produce a report. 6. If *Apply automatically* is ticked, changes are committed automatically. Otherwise, you have to press *Apply* after each change. Examples -------- First, let's see what is the output of the **Continuize** widget. We feed the original data (the *Heart disease* data set) into the [Data Table](../data/datatable) and see how they look like. Then we continuize the discrete values and observe them in another [Data Table](../data/datatable). ![](images/Continuize-Example1.png) In the second example, we show a typical use of this widget - in order to properly plot the linear projection of the data, discrete attributes need to be converted to continuous ones and that is why we put the data through the **Continuize** widget before drawing it. The attribute "*chest pain*" originally had four values and was transformed into three continuous attributes; similar happened to gender, which was transformed into a single attribute "*gender=female*". ![](images/Continuize-Example2.png)