# Logistic Regression

**Main Idea:**Find the parameters for a line that partitions a data set.

#### General Approach (MLIA: p. 84)

- Collect Data: any method
- Prepare Data: Convert to numeric data if needed.
- Analyze: Any method.
- Train: Find the optimal coefficients to classify the data.
- Use: Given new data, classify it based on the previously classified data.

#### Pros, Cons, and Data Types

**Pros:**

- Computationally Cheap
- Easy to implement
- Easy to interpret

**Cons:**

- Succeptible to overfitting
- Not always accurate

**Data Types:**

- Numeric Values
- Nominal Values

##### Sidebar on Nominal Values

*Nominal Values*are data that you can determine to be equivalent to other data or belonging to a set of data, but no ordering or other numeric calculations are possible.

Dichotomous: Belongs to one of two groupsNon-Dichotomous: Belongs to one of multiple groups

- Nominal Values are usually summarized using frequencies or percentages (and sometimes summarized by mode).
- Column (bar) charts are the best form of graphical representation (along with pie charts)
- These are also called
*categorical*or*qualitative*values