ConfigBinaryClassifier

ByJG\TextClassifier\ConfigBinaryClassifier controls the classification algorithm of the BinaryClassifier spam filter. All setters return $this for fluent chaining.

Parameters

Parameter	Setter	Default	Type	Description
use_relevant	setUseRelevant(int)	15	int	Maximum number of tokens used during classification. Only the tokens deviating most from 0.5 are used.
min_dev	setMinDev(float)	0.2	float	Minimum deviation from 0.5 required for a token to be considered relevant. Tokens with abs(0.5 - p) < min_dev are ignored.
rob_s	setRobS(float)	0.3	float	Robinson smoothing weight. Higher values give more weight to the neutral prior when token data is scarce.
rob_x	setRobX(float)	0.5	float	Robinson neutral prior. The assumed spam probability for a token never seen before. 0.5 = completely neutral.

Usage

use ByJG\TextClassifier\ConfigBinaryClassifier;

$config = (new ConfigBinaryClassifier())
    ->setUseRelevant(20)
    ->setMinDev(0.1)
    ->setRobS(0.3)
    ->setRobX(0.5);

Tuning guidance

use_relevant

Increasing this value allows more tokens to contribute to the score, which can improve accuracy on long texts. Decreasing it makes the filter focus only on the strongest signals. Values between 10 and 25 are typical.

min_dev

Lowering this threshold includes more borderline tokens. Setting it to 0 uses all tokens, including those the filter is uncertain about. Raising it makes the filter rely only on very clear spam or ham signals.

rob_s and rob_x

These control how the filter handles tokens with little training data:

• rob_s higher → rare tokens are pulled more strongly toward rob_x (less influence from sparse data)
• rob_x = 0.5 → no bias for unknown tokens (recommended)
• rob_x > 0.5 → bias toward spam for unknown tokens (aggressive)
• rob_x < 0.5 → bias toward ham for unknown tokens (permissive)

Getters

Method	Returns
getUseRelevant()	int
getMinDev()	float
getRobS()	float
getRobX()	float