diff --git a/README.md b/README.md
index 22339b9..c583ef3 100644
--- a/README.md
+++ b/README.md
@@ -55,8 +55,10 @@ func main() {
 	cls.Fit(trainData)
 
 	//Calculates the Euclidean distance and returns the most popular label
-	predictions := cls.Predict(testData)
-	fmt.Println(predictions)
+	predictions, err := cls.Predict(testData)
+	if err != nil {
+		panic(err)
+	}
 
 	// Prints precision/recall metrics
 	confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
diff --git a/examples/knnclassifier/knnclassifier_iris.go b/examples/knnclassifier/knnclassifier_iris.go
index 726d242..592e3ee 100644
--- a/examples/knnclassifier/knnclassifier_iris.go
+++ b/examples/knnclassifier/knnclassifier_iris.go
@@ -2,6 +2,7 @@ package main
 
 import (
 	"fmt"
+
 	"github.com/sjwhitworth/golearn/base"
 	"github.com/sjwhitworth/golearn/evaluation"
 	"github.com/sjwhitworth/golearn/knn"
@@ -21,7 +22,10 @@ func main() {
 	cls.Fit(trainData)
 
 	//Calculates the Euclidean distance and returns the most popular label
-	predictions := cls.Predict(testData)
+	predictions, err := cls.Predict(testData)
+	if err != nil {
+		panic(err)
+	}
 	fmt.Println(predictions)
 
 	// Prints precision/recall metrics
diff --git a/knn/knn.go b/knn/knn.go
index 1770c0d..bbbd43d 100644
--- a/knn/knn.go
+++ b/knn/knn.go
@@ -4,7 +4,9 @@
 package knn
 
 import (
+	"errors"
 	"fmt"
+
 	"github.com/gonum/matrix"
 	"github.com/gonum/matrix/mat64"
 	"github.com/sjwhitworth/golearn/base"
@@ -34,8 +36,9 @@ func NewKnnClassifier(distfunc string, neighbours int) *KNNClassifier {
 }
 
 // Fit stores the training data for later
-func (KNN *KNNClassifier) Fit(trainingData base.FixedDataGrid) {
+func (KNN *KNNClassifier) Fit(trainingData base.FixedDataGrid) error {
 	KNN.TrainingData = trainingData
+	return nil
 }
 
 func (KNN *KNNClassifier) canUseOptimisations(what base.FixedDataGrid) bool {
@@ -89,7 +92,7 @@ func (KNN *KNNClassifier) canUseOptimisations(what base.FixedDataGrid) bool {
 }
 
 // Predict returns a classification for the vector, based on a vector input, using the KNN algorithm.
-func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
+func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error) {
 	// Check what distance function we are using
 	var distanceFunc pairwise.PairwiseDistanceFunc
 	switch KNN.DistanceFunc {
@@ -98,20 +101,20 @@ func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
 	case "manhattan":
 		distanceFunc = pairwise.NewManhattan()
 	default:
-		panic("unsupported distance function")
+		return nil, errors.New("unsupported distance function")
 	}
 	// Check Compatibility
 	allAttrs := base.CheckCompatible(what, KNN.TrainingData)
 	if allAttrs == nil {
 		// Don't have the same Attributes
-		return nil
+		return nil, errors.New("attributes not compatible")
 	}
 
 	// Use optimised version if permitted
 	if KNN.AllowOptimisations {
 		if KNN.DistanceFunc == "euclidean" {
 			if KNN.canUseOptimisations(what) {
-				return KNN.optimisedEuclideanPredict(what.(*base.DenseInstances))
+				return KNN.optimisedEuclideanPredict(what.(*base.DenseInstances)), nil
 			}
 		}
 	}
@@ -189,7 +192,11 @@ func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
 
 	})
 
-	return ret
+	return ret, nil
+}
+
+func (KNN *KNNClassifier) String() string {
+	return fmt.Sprintf("KNNClassifier(%s, %d)", KNN.DistanceFunc, KNN.NearestNeighbours)
 }
 
 func (KNN *KNNClassifier) vote(maxmap map[string]int, values []int) string {
diff --git a/knn/knn_bench_test.go b/knn/knn_bench_test.go
index 753602d..4d49631 100644
--- a/knn/knn_bench_test.go
+++ b/knn/knn_bench_test.go
@@ -2,9 +2,10 @@ package knn
 
 import (
 	"fmt"
+	"testing"
+
 	"github.com/sjwhitworth/golearn/base"
 	"github.com/sjwhitworth/golearn/evaluation"
-	"testing"
 )
 
 func readMnist() (*base.DenseInstances, *base.DenseInstances) {
@@ -45,7 +46,10 @@ func BenchmarkKNNWithOpts(b *testing.B) {
 	cls := NewKnnClassifier("euclidean", 1)
 	cls.AllowOptimisations = true
 	cls.Fit(train)
-	predictions := cls.Predict(test)
+	predictions, err := cls.Predict(test)
+	if err != nil {
+		b.Error(err)
+	}
 	c, err := evaluation.GetConfusionMatrix(test, predictions)
 	if err != nil {
 		panic(err)
@@ -60,7 +64,10 @@ func BenchmarkKNNWithNoOpts(b *testing.B) {
 	cls := NewKnnClassifier("euclidean", 1)
 	cls.AllowOptimisations = false
 	cls.Fit(train)
-	predictions := cls.Predict(test)
+	predictions, err := cls.Predict(test)
+	if err != nil {
+		b.Error(err)
+	}
 	c, err := evaluation.GetConfusionMatrix(test, predictions)
 	if err != nil {
 		panic(err)
diff --git a/knn/knn_test.go b/knn/knn_test.go
index 0ba6570..7b72453 100644
--- a/knn/knn_test.go
+++ b/knn/knn_test.go
@@ -1,9 +1,10 @@
 package knn
 
 import (
+	"testing"
+
 	"github.com/sjwhitworth/golearn/base"
 	. "github.com/smartystreets/goconvey/convey"
-	"testing"
 )
 
 func TestKnnClassifierWithoutOptimisations(t *testing.T) {
@@ -17,7 +18,8 @@ func TestKnnClassifierWithoutOptimisations(t *testing.T) {
 		cls := NewKnnClassifier("euclidean", 2)
 		cls.AllowOptimisations = false
 		cls.Fit(trainingData)
-		predictions := cls.Predict(testingData)
+		predictions, err := cls.Predict(testingData)
+		So(err, ShouldBeNil)
 		So(predictions, ShouldNotEqual, nil)
 
 		Convey("When predicting the label for our first vector", func() {
@@ -47,7 +49,8 @@ func TestKnnClassifierWithOptimisations(t *testing.T) {
 		cls := NewKnnClassifier("euclidean", 2)
 		cls.AllowOptimisations = true
 		cls.Fit(trainingData)
-		predictions := cls.Predict(testingData)
+		predictions, err := cls.Predict(testingData)
+		So(err, ShouldBeNil)
 		So(predictions, ShouldNotEqual, nil)
 
 		Convey("When predicting the label for our first vector", func() {
@@ -75,7 +78,8 @@ func TestKnnClassifierWithTemplatedInstances1(t *testing.T) {
 
 		cls := NewKnnClassifier("euclidean", 2)
 		cls.Fit(trainingData)
-		predictions := cls.Predict(testingData)
+		predictions, err := cls.Predict(testingData)
+		So(err, ShouldBeNil)
 		So(predictions, ShouldNotBeNil)
 	})
 }
@@ -89,7 +93,16 @@ func TestKnnClassifierWithTemplatedInstances1Subset(t *testing.T) {
 
 		cls := NewKnnClassifier("euclidean", 2)
 		cls.Fit(trainingData)
-		predictions := cls.Predict(testingData)
+		predictions, err := cls.Predict(testingData)
+		So(err, ShouldBeNil)
 		So(predictions, ShouldNotBeNil)
 	})
 }
+
+func TestKnnClassifierImplementsClassifier(t *testing.T) {
+	cls := NewKnnClassifier("euclidean", 2)
+	var c base.Classifier = cls
+	if len(c.String()) < 1 {
+		t.Fail()
+	}
+}