function predict( X, type ) {
		var sh;
		var t;
		if ( !isndarrayLike( X ) ) {
			throw new TypeError( format( 'invalid argument. First argument must be an ndarray. Value: `%s`.', X ) );
		}
		sh = X.shape;
		if ( sh[ sh.length-1 ] !== N ) {
			throw new TypeError( format( 'invalid argument. First argument must be an ndarray whose last dimension is of size %u. Actual size: `%u`.', N, sh[ sh.length-1 ] ) );
		}
		t = 'label';
		if ( arguments.length > 1 ) {
			if ( type === 'probability' ) {
				if ( opts.loss !== 'log' && opts.loss !== 'modifiedHuber' ) {
					throw new Error( format( 'invalid argument. Second argument is incompatible with model loss function. Probability predictions are only supported when the loss function is one of the following: "%s". Model loss function: `%s`.', [ 'log', 'modifiedHuber' ].join( '", "' ), opts.loss ) );
				}
			} else if ( type !== 'label' && type !== 'linear' ) {
				throw new TypeError( format( 'invalid argument. Second argument must be a string value equal to either "label", "probability", or "linear". Value: `%s`.', type ) );
			}
			t = type;
		}
		return model.predict( X, t );
	}
Predicts the response value for one or more observation vectors `X`.

@private
@param {ndarrayLike} X - ndarray (of size `(...,N)`) containing observation vectors
@param {string} [type="label"] - prediction type (either `label`, `probability`, or `linear`)
@throws {TypeError} first argument must be an ndarray
@throws {TypeError} first argument must be an ndarray whose last dimension matches the number of model features
@throws {TypeError} second argument must be a recognized/supported prediction "type"
@throws {Error} second argument must be compatible with the model loss function
@returns {ndarray} ndarray (of size `(...)`) containing response values

@example
var Float64Array = require( '@stdlib/array/float64' );
var array = require( '@stdlib/ndarray/array' );

// Create an accumulator:
var accumulator = incrBinaryClassification( 3 );

// ...

// Create a new observation vector:
var x = array( new Float64Array( [ 2.3, 5.3, 8.6 ] ) );

// Predict the response value:
var yhat = accumulator.predict( x );
// returns <ndarray>