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	return {
		'class'  => $best,
		'scores' => \%scores,
		'probs'  => \%probs,
		'priors' => \%priors,
		'tokens' => \%token_info,
	};
} ## end sub explain

=head2 tweak

Changes scoring settings on a existing model. Takes the args below,
all optional, but at least one must be specified.

    smoothing - The smoothing to use... laplace or lidstone.

    alpha - The alpha to use for lidstone smoothing. Must be a number
        greater than 0. May only be specified when the resulting
        smoothing is lidstone.

    priors - How class priors are computed... trained or uniform.

    # switch to lidstone smoothing with a alpha of 0.1
    $nb->tweak( 'smoothing' => 'lidstone', 'alpha' => 0.1 );

    # switch to uniform priors
    $nb->tweak( 'priors' => 'uniform' );

These are safe to change after training as they only affect scoring,
not the trained counts. Settings that shape the trained data, such as
ngrams, token_weighting, and the tokenizer settings, may not be
changed here as that would make the model inconsistent with what was
trained... for those, create a new object and retrain.

Only args specified with a defined value are changed. Args passed
with a undef value are ignored, so it is safe to pass through
possibly unset values.

Switching smoothing to laplace sets alpha to 1, as laplace is add-one.
Switching to lidstone without specifying alpha keeps the current
alpha.

Will die if passed a unknown arg, no args with defined values, or a
insane value. If it dies, the model is left unchanged.

=cut

sub tweak {
	my ( $self, %args ) = @_;

	my %known_args = ( 'smoothing' => 1, 'alpha' => 1, 'priors' => 1 );
	foreach my $arg ( keys %args ) {
		if ( !defined( $known_args{$arg} ) ) {
			die( '"' . $arg . '" is not a known arg' );
		}
	}
	if ( !grep { defined( $args{$_} ) } keys %args ) {
		die('No args specified');
	}

	# validate against what the settings would become
	my $smoothing = defined( $args{'smoothing'} ) ? $args{'smoothing'} : $self->{'model'}{'smoothing'};
	if ( !defined($smoothing) ) {
		$smoothing = 'laplace';
	}
	if ( $smoothing ne 'laplace' && $smoothing ne 'lidstone' ) {
		die( 'smoothing must be either "laplace" or "lidstone" and not "' . $smoothing . '"' );
	}

	if ( defined( $args{'alpha'} ) ) {
		if ( $smoothing eq 'laplace' ) {
			die('alpha may only be specified when the resulting smoothing is lidstone');
		}
		if ( ref( $args{'alpha'} ) ne '' || $args{'alpha'} !~ /\A\d*\.?\d+\z/ || $args{'alpha'} <= 0 ) {
			die('alpha must be a number greater than 0');
		}
	}

	if ( defined( $args{'priors'} ) && $args{'priors'} ne 'trained' && $args{'priors'} ne 'uniform' ) {
		die( 'priors must be either "trained" or "uniform" and not "' . $args{'priors'} . '"' );
	}

	# only change what was specified with a defined value
	if ( defined( $args{'smoothing'} ) ) {
		$self->{'model'}{'smoothing'} = $args{'smoothing'};
		if ( $args{'smoothing'} eq 'laplace' ) {
			# laplace is add-one, so alpha is always 1
			$self->{'model'}{'alpha'} = 1;
		}
	}
	if ( defined( $args{'alpha'} ) ) {
		$self->{'model'}{'alpha'} = $args{'alpha'};
	}
	if ( defined( $args{'priors'} ) ) {
		$self->{'model'}{'priors'} = $args{'priors'};
	}
} ## end sub tweak

=head2 to_string

Returns the model as a JSON string. See the section MODEL FORMAT for
what the JSON looks like.

    my $json = $nb->to_string;

The JSON is generated with canonical set, so the keys are sorted,
meaning two calls against the same model will always produce identical
output, making it diffable.

If token_splitter or stop_regex was set to a qr// Regexp, it is
stringified, so the result is always JSON safe.

=cut

sub to_string {
	my ($self) = @_;

	# qr// Regexps can't be JSON encoded, so stringify them
	my %model = %{ $self->{'model'} };
	foreach my $regex_item ( 'token_splitter', 'stop_regex' ) {
		if ( ref( $model{$regex_item} ) eq 'Regexp' ) {