LINEAR_REG

Executes linear regression on an input relation, and returns a linear regression model.

Syntax

LINEAR_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 SYNC_WITH_HCATALOG_SCHEMA to sync the hcatalog schema, and then run the machine learning function.
response‑column

Name of the input column that represents the dependent variable or outcome. All values in this column must be numeric, otherwise the model is invalid.
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 exclude_columns must include response‑column, and any columns that are invalid as predictor columns.

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:

  • Newton
  • BFGS

  • CGD

    If you select CGD, regularization‑method must be set to L1 or ENet, otherwise the function returns an error.

Default: CGD if regularization‑method is set to L1 or ENet, otherwise Newton.
regularization

Determines the method of regularization, one of the following:

  • None (default)
  • L1
  • L2
  • ENet
epsilon

Determines whether the algorithm has reached the specified accuracy result.

Default: 1e-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. The value must be zero or non-negative.

Default: 1
alpha

ENet mixture parameter that defines how much L1 versus L2 regularization to provide. This argument sends a warning if used without ENet regularization.

Valid Values: [0,1]

A value of 1 is equivalent to L1 and a value of 0 is equivalent to L2.

Model Attributes

Attribute Description
data

The data for the function, including:

  • coeffNames: Name of the coefficients. This starts with intercept and then follows with the names of the predictors in the same order specified in the call.
  • coeff: Vector of estimated coefficients, with the same order as coeffNames
  • stdErr: Vector of the standard error of the coefficients, with the same order as coeffNames
  • zValue (for logistic regression): Vector of z-values of the coefficients, in the same order as coeffNames
  • tValue (for linear regression): Vector of t-values of the coefficients, in the same order as coeffNames
  • pValue: Vector of p-values of the coefficients, in the same order as coeffNames
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.

Examples

=> SELECT LINEAR_REG('myLinearRegModel', 'faithful', 'eruptions', 'waiting' 
                      USING PARAMETERS optimizer='BFGS');
         LINEAR_REG
----------------------------
 Finished in 10 iterations

(1 row)

See Also