diff --git a/src/linear_model/LogisticRegression.test.ts b/src/linear_model/LogisticRegression.test.ts
index 1d38ba5..80b3964 100644
--- a/src/linear_model/LogisticRegression.test.ts
+++ b/src/linear_model/LogisticRegression.test.ts
@@ -47,6 +47,68 @@ describe('LogisticRegression', function () {
     expect(results.arraySync()).toEqual([0, 0, 0, 1, 1, 1])
     expect(logreg.score(X, y) > 0.5).toBe(true)
   }, 30000)
+  it('Test of the function used with 2 classes (one hot)', async function () {
+    let X = [
+      [0, -1],
+      [1, 0],
+      [1, 1],
+      [1, -1],
+      [2, 0],
+      [2, 1],
+      [2, -1],
+      [3, 2],
+      [0, 4],
+      [1, 3],
+      [1, 4],
+      [1, 5],
+      [2, 3],
+      [2, 4],
+      [2, 5],
+      [3, 4]
+    ]
+    let y = [
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [0, 1],
+      [0, 1],
+      [0, 1],
+      [0, 1],
+      [0, 1],
+      [0, 1],
+      [0, 1],
+      [0, 1]
+    ]
+
+    let Xtest = [
+      [0, -2],
+      [1, 0.5],
+      [1.5, -1],
+      [1, 4.5],
+      [2, 3.5],
+      [1.5, 5]
+    ]
+
+    let logreg = new LogisticRegression({ penalty: 'none' })
+    await logreg.fit(X, y)
+    let probabilities = logreg.predictProba(X)
+    expect(probabilities instanceof tf.Tensor).toBe(true)
+    let results = logreg.predict(Xtest) // compute results of the training set
+    expect(results.arraySync()).toEqual([
+      [1, 0],
+      [1, 0],
+      [1, 0],
+      [0, 1],
+      [0, 1],
+      [0, 1]
+    ])
+    expect(logreg.score(X, y) > 0.5).toBe(true)
+  }, 30000)
   it('Test of the prediction with 3 classes', async function () {
     let X = [
       [0, -1],
diff --git a/src/linear_model/SgdClassifier.ts b/src/linear_model/SgdClassifier.ts
index 56a55e1..bc9ee15 100644
--- a/src/linear_model/SgdClassifier.ts
+++ b/src/linear_model/SgdClassifier.ts
@@ -13,7 +13,10 @@
 * ==========================================================================
 */
 
-import { convertToNumericTensor1D, convertToNumericTensor2D } from '../utils'
+import {
+  convertToNumericTensor1D_2D,
+  convertToNumericTensor2D
+} from '../utils'
 import {
   Scikit2D,
   Scikit1D,
@@ -23,8 +26,7 @@ import {
   Tensor2D,
   Tensor,
   ModelCompileArgs,
-  ModelFitArgs,
-  RecursiveArray
+  ModelFitArgs
 } from '../types'
 import { OneHotEncoder } from '../preprocessing/OneHotEncoder'
 import { assert } from '../typesUtils'
@@ -103,6 +105,7 @@ export class SGDClassifier extends ClassifierMixin {
   lossType: LossTypes
   oneHot: OneHotEncoder
   tf: any
+  isMultiOutput: boolean
 
   constructor({
     modelFitArgs,
@@ -119,6 +122,7 @@ export class SGDClassifier extends ClassifierMixin {
     this.denseLayerArgs = denseLayerArgs
     this.optimizerType = optimizerType
     this.lossType = lossType
+    this.isMultiOutput = false
     // Next steps: Implement "drop" mechanics for OneHotEncoder
     // There is a possibility to do a drop => if_binary which would
     // squash down on the number of variables that we'd have to learn
@@ -200,12 +204,17 @@ export class SGDClassifier extends ClassifierMixin {
    * // lr model weights have been updated
    */
 
-  public async fit(X: Scikit2D, y: Scikit1D): Promise<SGDClassifier> {
+  public async fit(
+    X: Scikit2D,
+    y: Scikit1D | Scikit2D
+  ): Promise<SGDClassifier> {
     let XTwoD = convertToNumericTensor2D(X)
-    let yOneD = convertToNumericTensor1D(y)
+    let yOneD = convertToNumericTensor1D_2D(y)
 
     const yTwoD = this.initializeModelForClassification(yOneD)
-
+    if (yOneD.shape.length > 1) {
+      this.isMultiOutput = true
+    }
     if (this.model.layers.length === 0) {
       this.initializeModel(XTwoD, yTwoD)
     }
@@ -344,6 +353,9 @@ export class SGDClassifier extends ClassifierMixin {
   public predict(X: Scikit2D): Tensor1D {
     assert(this.model.layers.length > 0, 'Need to call "fit" before "predict"')
     const y2D = this.predictProba(X)
+    if (this.isMultiOutput) {
+      return this.tf.oneHot(y2D.argMax(1), y2D.shape[1])
+    }
     return this.tf.tensor1d(this.oneHot.inverseTransform(y2D))
   }
 
@@ -418,10 +430,4 @@ export class SGDClassifier extends ClassifierMixin {
 
     return intercept
   }
-
-  private getModelWeight(): Promise<RecursiveArray<number>> {
-    return Promise.all(
-      this.model.getWeights().map((weight: any) => weight.array())
-    )
-  }
 }
diff --git a/src/mixins.ts b/src/mixins.ts
index 2d71b81..225f7f7 100644
--- a/src/mixins.ts
+++ b/src/mixins.ts
@@ -1,6 +1,8 @@
 import { Scikit2D, Scikit1D, Tensor2D, Tensor1D } from './types'
 import { r2Score, accuracyScore } from './metrics/metrics'
 import { Serialize } from './simpleSerializer'
+import { assert, isScikit2D } from './typesUtils'
+import { convertToNumericTensor1D_2D } from './utils'
 export class TransformerMixin extends Serialize {
   // We assume that fit and transform exist
   [x: string]: any
@@ -35,8 +37,17 @@ export class ClassifierMixin extends Serialize {
   [x: string]: any
 
   EstimatorType = 'classifier'
-  public score(X: Scikit2D, y: Scikit1D): number {
+  public score(X: Scikit2D, y: Scikit1D | Scikit2D): number {
     const yPred = this.predict(X)
+    const yTrue = convertToNumericTensor1D_2D(y)
+    assert(
+      yPred.shape.length === yTrue.shape.length,
+      "The shape of the model output doesn't match the shape of the actual y values"
+    )
+
+    if (isScikit2D(y)) {
+      return accuracyScore(yTrue.argMax(1) as Scikit1D, yPred.argMax(1))
+    }
     return accuracyScore(y, yPred)
   }
 }

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