LOGISTIC_REG
Executes logistic regression on an input relation. The result is a logistic regression model.
Syntax
LOGISTIC_REG ( 'model‑name', 'input‑relation', 'response‑column', 'predictor‑columns' [ USING PARAMETERS [exclude_columns='excluded‑columns'] [, optimizer='optimizer‑method'] [, regularization='regularization‑method'] [, epsilon=epsilon‑value] [, max_iterations=iterations] [, lambda=lamda‑value] [, alpha=alpha‑value] ] )
Arguments
model‑name |
Identifies the model to create, where model‑name conforms to conventions described in Identifiers. It must also be unique among all names of sequences, tables, projections, views, and models within the same schema. |
input‑relation |
The table or view that contains the training data for building the model. If the input relation is defined in Hive, use |
response‑column |
The input column that represents the dependent variable or outcome. The column value must be 0 or 1, and of type numeric or BOOLEAN. The function automatically skips all other values. |
predictor‑columns |
Comma-separated list of columns in the input relation that represent independent variables for the model, or asterisk (*) to select all columns. If you select all columns, the argument list for parameter All predictor columns must be of type numeric or BOOLEAN; otherwise the model is invalid. All BOOLEAN predictor values are converted to FLOAT values before training: 0 for false, 1 for true. No type checking occurs during prediction, so you can use a BOOLEAN predictor column in training, and during prediction provide a FLOAT column of the same name. In this case, all FLOAT values must be either 0 or 1. |
Parameter Settings
Parameter name | Set to… |
---|---|
exclude_columns
|
Comma-separated list of columns from predictor‑columns to exclude from processing. |
optimizer
|
The optimizer method used to train the model, one of the following:
Default: |
regularization
|
Determines the method of regularization, one of the following:
|
epsilon
|
Determines whether the algorithm has reached the specified accuracy result. Default: 1 e-6 |
max_iterations
|
Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result. Default: 100 |
lambda
|
The regularization parameter value, an integer ≥ 0. Default: 1 |
alpha
|
ENet mixture parameter that defines how much L1 versus L2 regularization to provide, one of the following:
This argument returns a warning if it is used without ENet regularization. |
Model Attributes
Attribute | Description |
---|---|
data
|
The data for the function, including:
|
regularization
|
The type of regularization to use when training the model. |
lambda
|
The regularization parameter. Higher values enforce stronger regularization. This value must be positive. |
alpha
|
The elastic net mixture parameter. |
iterations
|
The number of iterations that actually occur for the convergence before exceeding max_iteration. |
skippedRows
|
The number of rows of input_relation that were skipped because they contained an invalid value. |
processedRows
|
The total number of rows in input_relation minus the skippedRows . |
callStr
|
The value of all input arguments that were specified at the time the function was called. |
Privileges
Superuser, or SELECT privileges on the input relation
Examples
=> SELECT LOGISTIC_REG('myLogisticRegModel', 'mtcars', 'am', 'mpg, cyl, disp, hp, drat, wt, qsec, vs, gear, carb' USING PARAMETERS exclude_columns='hp', optimizer='BFGS'); LOGISTIC_REG ---------------------------- Finished in 20 iterations (1 row)